case study of depression in pakistan

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June 01, 2024 | VOL. 181, NO. 6 CURRENT ISSUE pp.461-564
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Depression in a Pakistani Woman

Anita Aijaz , F.C.P.S.(Psych.) , and
Uzma Ambareen , M.D.

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“Ms. N,” a 27-year-old Muslim, Sindhi-speaking, married woman, born in a small village in Pakistan, the sixth of seven children, came to the city of Karachi after getting married 6 years ago. She is the second wife of a landlord and lives with him and their three children in a flat on the third floor of an old building. She arrived for her first appointment with a psychiatrist accompanied by her husband and their 3-year-old son. When the psychiatrist asked her the reason for her visit, she looked at her husband and said to him, in Sindhi, “You tell her.” The psychiatrist then addressed her in Sindhi (her own mother tongue), and the patient began to speak: “Doctor, I have become very sensitive. I start crying whenever I hear any bad news. I was not like this before.” She also complained of being unable to sleep, having no interest in anything, feeling as if there were no life in her body, and having difficulty in performing housework and looking after her children. She reported disturbing dreams in which she was surrounded by strange people and was anxiously searching for someone. She also complained of irritability, headaches, body aches, and feeling tired all the time. She had thought of seeing a spiritual healer (a murshid ) in her village who had previously helped her, through prayers. During a previous episode of depression, she had been taken to see another spiritual healer ( pir sahib ), who told her that she was under the influence of the evil eye ( nazar ), and that her body needed to be purified. She had then gone to see him every Friday (the Islamic sabbath) with her family for various healing rituals, including recitation of Arabic verses from the Koran.

Being away from her village, when she recently became depressed, she visited a general practitioner, who prescribed an antidepressant, but she was reluctant to take it because she believed that it would be addictive. However, she continued to take the prescribed benzodiazepine, which helped her sleep but produced no other benefits, so her physician referred her to the psychiatrist.

Moving to Karachi from her village after marriage was an enormous cultural shock for her. She felt insecure in this new environment, where she frequently heard news of muggings, armed robberies, cellphone snatching, and killings. There was no one here she could share her feelings with. From her village home, with its open courtyard and plenty of fresh air, she had moved to a small flat in the city, surrounded by tall buildings, polluted air, and noisy traffic. She was not fluent in the language used by her neighbors, and women in the neighborhood made fun of her because she was simple and different. She felt humiliated by that but avoided telling her husband because he would just tell her that she did not know how to deal with people. Her self-esteem and confidence level had become very low. Over time, her language and social skills improved, but she made only one friend, and they socialized mostly with families from similar social backgrounds.

She was having a hard time coping with her fear about the lawlessness and sense of insecurity in the city, and because of this, she avoided watching the news on television, which her husband was very fond of. She recounted a recent incident, when, during a shopping trip, she witnessed the body of a shooting victim, draped in a white cloth, being put into an ambulance, while all around, people were going about their business as if nothing had happened. She could not get that image out of her mind for a long time, and she felt very disturbed by people’s insensitivity.

Ms. N’s mother had a stroke 2 years ago, but Ms. N could not stay in the village to take care of her. Then when her mother died, she could not travel to the village for her funeral because of security concerns. It took her a while to come to terms with it.

Ms. N’s parents had five daughters before her mother became pregnant with her. They desperately wanted a son and were bitterly disappointed when Ms. N was born. Her eldest sister told her that after Ms. N’s birth, their parents had quarreled and her mother had initially refused to breastfeed her, saying, “Let her die.” As she grew up, her mother paid very little attention to her. Her father, a schoolteacher, was a heavy cannabis user and never showed affection toward his wife or daughters. Initially, Ms. N thought that these behaviors were “normal,” but she felt confused when she saw both parents showering love and attention on her baby brother. Ms. N often pretended to be her brother, in order to get attention from her father.

Ms. N recalled that at one time, her mother had been experiencing the symptoms that she herself was now experiencing.

Ms. N used to have a few friends in the village, and her closest friend was a cousin. She had wanted to marry another cousin, but when he became betrothed to someone else, she was heartbroken and decided that she would resume her studies and enroll in college instead of getting married. However, her family refused to accept this, and her brother told her that she had only three honorable options: to marry a man chosen by her family, to discontinue her studies and stay at home, or to commit suicide. She chose the third option by cutting a vein on the back of her hand, but the cut was not deep and she survived. In the days that followed, she felt lonely, betrayed, rejected, and defeated. It was at that time that she was taken to a spiritual healer because her family was convinced that because of her beauty, someone had cast an evil eye ( nazar ) on her.

On examination, Ms. N appeared to be a young and attractive woman, well groomed and traditionally attired. She was pleasant and cooperative, and she provided clear and precise responses in a soft voice. She appeared sad and was tearful at times. There was no evidence of psychosis, obsessive-compulsive symptoms, or violent thoughts. She acknowledged that she did not want to live, but noted that she would not kill herself since suicide is considered to be a major sin in Islam, and also because her children were still very young and needed her care. She believed that her problems were a result of kismet , a dark fate ( kismet is a Turkish word used commonly in Pakistan). Her cognitive functions were within normal limits.

Ms. N was diagnosed with major depressive disorder. Her psychiatrist prescribed sertraline, to be titrated to 50 mg/day over a 2-week period. The psychiatrist told her that sertraline is not addictive and that it corrects a chemical imbalance in depression. Initially, Ms. N contacted her psychiatrist frequently with complaints of side effects, but these decreased gradually and her depressive symptoms began improving. She was also referred for counseling, which she declined because she could not afford it. She was encouraged to start regular walks, but she never managed to get started on a walking program. She had considered seeing a spiritual healer again but was no longer sure it was the right thing to do. She did visit a shrine for prayers once during her treatment. She started attending a personal grooming course in her neighborhood, and made friends with several women there.

She came for her follow-up appointments rather reluctantly because she was afraid that if her in-laws found out about her psychiatric treatment, they would think of her as being “crazy.”

The improvement initially seen in her condition was maintained over the next few months.

Ms. N met criteria for major depressive disorder of moderate severity. Predisposing factors included a family history of depression, adverse early experiences, including parental discord, gender discrimination, and emotional neglect. Her marriage against her wishes and then the move to Karachi and its accompanying problems were the most likely precipitating factors.

Prevailing insecurity in the city was an important perpetuating factor for her depression, as chronic terrorist attacks have a significant impact on mental health, often causing anxiety and depression. Women especially are concerned about the threat of terrorism, and they think more about terrorism than men do ( 1 ).

In South Asian cultures, somatic symptoms are a common presenting complaint in depressed patients, especially women ( 2 ), since physical illnesses are more culturally acceptable and more often given proper attention, as compared with psychiatric illnesses. Thus, patients with depression are usually seen initially by general practitioners, and the diagnosis is often missed. In Ms. N’s case, headaches and body aches were major complaints.

Some 70% of Pakistan’s population resides in rural areas, within an established feudal or tribal value system. Awareness about mental health is limited. Mental illnesses are usually stigmatized and are perceived to have supernatural causes ( 3 ). Hence, the majority of patients seek help from traditional faith healers or religious leaders ( 4 ).

Suicide is a condemned act in Islam ( 5 ), and under Pakistani law (based on the tenets of Islam) both suicide and deliberate self-harm are illegal acts, punishable by imprisonment and fines ( 6 ). Nevertheless, the number of suicides has been increasing in recent years. Most suicides occur in single men and married women under age 30. The most common methods used are hanging, insecticide, and firearm, and the most common reasons for suicide are interpersonal relationship problems and domestic issues ( 7 ). Suicide in Pakistan is strongly associated with depression, which is underrecognized and undertreated ( 8 ). In most cases, a psychiatric history is not available.

Paradoxically, in certain subcultures, suicide is considered to be an honorable act in specific situations. Women are seen as responsible for maintaining a family’s honor, and Ms. N was told by her brother to either follow the family’s wishes or commit suicide. In other words, a woman who disobeys her family’s wishes is essentially bringing dishonor to her family, and the only way to salvage that honor is to commit suicide. Women who are raped or are in a situation where they believe that rape is impending sometimes prefer to kill themselves rather than subject themselves and their families to this dishonor and shame.

The authors report no financial relationships with commercial interests.

1 Khan AM, Sarhandi I, Hussain J, Iqbal S, Taj R : Impact of terrorism on mental health . Annals of Pakistan Institute of Medical Sciences 2012 ; 8:46–49 Google Scholar

2 Chaudhry HR, Arshad N, Javed F, Asif A : Frequency of psychological and somatic symptoms in patients with major depressive disorder . Asian J Psychiatry 2010 ; 3:152–154 Crossref , Medline , Google Scholar

3 Karim S, Saeed K, Rana MH, Mubbashar MH, Jenkins R : Pakistan mental health country profile . Int Rev Psychiatry 2004 ; 16:83–92 Crossref , Medline , Google Scholar

4 Naeem F, Ayub M, Javed Z, Irfan M, Haral F, Kingdon D : Stigma and psychiatric illness: a survey of attitude of medical students and doctors in Lahore, Pakistan . J Ayub Med Coll Abbottabad 2006 ; 18:46–49 Medline , Google Scholar

5 Khan MM, Hyder AA : Suicides in the developing world: case study from Pakistan . Suicide Life Threat Behav 2006 ; 36:76–81 Crossref , Medline , Google Scholar

6 Khan MM : Suicide and attempted suicide in Pakistan . Crisis 1998 ; 19:172–176 Crossref , Medline , Google Scholar

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8 Khan MM, Mahmud S, Karim MS, Zaman M, Prince M : Case-control study of suicide in Karachi, Pakistan . Br J Psychiatry 2008 ; 193:402–405 Crossref , Medline , Google Scholar

Unveiling shadows: analyzing suicide reporting in Muslim-majority countries vis-à-vis WHO’s media guidelines 20 October 2023 | CNS Spectrums, Vol. 63
Determinants of depression in women with chronic disease: Evidence from a sample of poor loan takers from Pakistan 22 July 2020 | Journal of Community Psychology, Vol. 48, No. 7
Journal of Religion and Health, Vol. 57, No. 6

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Risk factors, prevalence, and treatment of anxiety and depressive disorders in Pakistan: systematic review

Related content
Peer review
Ilyas Mirza ( ilyasmirza{at}blueyonder.co.uk ) , specialist registrar in adult psychiatry 1 ,
Rachel Jenkins , visiting professor and director 2
1 Royal London Hospital (St Clement's), London E3 4LL
2 WHO Collaborating Centre for Mental Health, Institute of Psychiatry, London SE5 8AF
Correspondence to: I Mirza, Larkswood Centre, Thorpe Coombe Hospital, London E17 3HP
Accepted 5 March 2004

Objectives To assess the available evidence on the prevalence, aetiology, treatment, and prevention of anxiety and depressive disorders in Pakistan.

Design Systematic review of published literature.

Studies reviewed 20 studies, of which 17 gave prevalence estimates and 11 discussed risk factors.

Main outcome measures Prevalence of anxiety and depressive disorders, risk factors, effects of treatment.

Results Factors positively associated with anxiety and depressive disorders were female sex, middle age, low level of education, financial difficulty, being a housewife, and relationship problems. Arguments with husbands and relational problems with in-laws were positively associated in 3/11 studies. Those who had close confiding relationships were less likely to have anxiety and depressive disorders. Mean overall prevalence of anxiety and depressive disorders in the community population was 34% (range 29-66% for women and 10-33% for men). There were no rigorously controlled trials of treatments for these disorders.

Conclusions Available evidence suggests a major social cause for anxiety and depressive disorders in Pakistan. This evidence is limited because of methodological problems, so caution must be exercised in generalising this to the whole of the population of Pakistan.

Introduction

Anxiety and depressive disorders are common in all regions of the world. 1 They constitute a substantial proportion of the global burden of disease, and are projected to form the second most common cause of disability by 2020. 2 This increased importance of non-communicable diseases such as anxiety and depressive disorders presents a particular challenge for low income countries, where infectious diseases and malnutrition are still rife and where only a low percentage of gross domestic product is allocated to health services. 3 These disorders are also important because of their economic consequences. 4

With an estimated population of 152 million, Pakistan is the sixth most populous country in the world. It is projected that, by 2050, the population will have increased to make it the fourth most populous country. 5 There is a need to develop an evidence base to aid policy development on tackling anxiety and depressive disorders. We therefore conducted a systematic review as no such work existed to our knowledge.

Our main questions were ( a ) what the estimated prevalence of anxiety and depressive disorders is in Pakistan and how this compares with estimates from other low income countries; ( b ) what the associated social, psychological, and biological factors are; and ( c ) what evidence exists for effectiveness of treatment or prevention in this population.

Data sources

Using the key words “Pakistan” and (“mental” or “depression” or “anxiety” or “psychiatric”), we searched the following bibliographic databases from the start of each of their time frames: Applied Social Sciences Index and Abstracts, Cumulative Index to Nursing and Allied Health, Cochrane Trials Register, Excerpta Medica, National Library of Medicine Gateway, Medline (Pubmed), PsycINFO, Science Citation Index, and Social Science Citation Index. We searched the reference lists of retrieved articles for relevant studies. We also searched www.Pakmedinet.com , a medical website. These searches were last repeated on 1 March 2002 to keep the review as current as possible. Additionally, we hand searched the Pakistan Journal of Clinical Psychiatry until 1995, when it ceased publication.

Study selection

We selected studies that were conducted within Pakistan and that focused on depression, depressive disorder, or anxiety disorder in adults (ages 18-65). Variables of interest were prevalence, vulnerability factors, protective factors, and effectiveness of treatment and prevention strategies.

Data extraction

Each study received a code based on the relevance of its abstract and title to the study questions. Studies or reviews directly addressing anxiety and depressive disorders were retrieved for data extraction. Potentially useful qualitative and quantitative studies, as well as review articles were also retrieved. (A complete list is available from the authors.)

Validity check

We assessed the methodological quality of the selected studies according to hierarchies of evidence and critical appraisal checklists. 6 Since relatively few studies addressed our study questions, we included all studies directly relevant to the questions regardless of their quality.

Study synthesis

A narrative synthesis of the extracted studies was performed to address the questions of the review.

We found 20 studies that directly addressed the questions of the review: 19 were cross sectional epidemiological surveys, and one was a case-control study. w1-w20 Seventeen gave prevalence estimates (n = 9170), while 11 discussed associated risk factors. We did not find any prospective study of the natural course of the disorder or a rigorously controlled study of any interventions. We found little qualitative work. Sample sizes ranged from 113 to 2620 in prevalence studies (mean 539.41, median 298).

Methods of included studies

Table 1 shows the methodological quality of the studies. Only three of the 11 prevalence studies published in local journals gave adequate details of methods. Because of this, it is difficult to comment on possible biases. Even when basic data were provided it is questionable how representative the study sample was of the population. 7 Diagnoses in all the studies were made by either a psychiatrist or a trained worker using a validated instrument, and thus seem to be of reasonably good quality.

Checklist for quality of studies included in systematic review of evidence on prevalence, aetiology, treatment, and prevention of anxiety and depressive disorders in Pakistan

View inline

Most of the studies discussed the generalisability of their findings but did not interpret any null findings. In the discussions, national comparisons were rarely made with findings of other national research groups; comparisons were usually with studies in other countries.

Prevalence of anxiety and depressive disorders

Table 2 lists the prevalence of anxiety and depressive disorders estimated in the studies. The overall mean prevalence in men and women in the six studies of random community samples (n = 2658) was 33.62%, with the point prevalence varying from 28.8% to 66% for women (overall mean 45.5%) and from 10% to 33% for men (overall mean 21.7%). Women aged 15-49 were studied in a paper with 28.8% prevalence, while young men with a mean age of 18 participated in a study reporting 33% prevalence. Only one study reported adjusted prevalence with 95% confidence intervals.

Details of studies included in systematic review with prevalence estimates of anxiety and depressive disorders

For those presenting to traditional or faith healers (n = 511), the prevalence of anxiety and depressive disorders among men varied from 2.65% to 27%, and among women from 11.5 % to 52%.

Three studies looked at total psychiatric morbidity in primary care (n = 774). One described women in a rural area, with a prevalence of 50%, while another described 18% prevalence for men and 42.2% for women in an urban area. The third study, with a prevalence of 38.4%, did not specify participants' sex.

Of those presenting to psychiatric outpatients (n = 2430), the prevalence varied between 32% and 66.3%. There were two studies on psychiatric inpatients, one reported a prevalence of depressive illness of 37% (n = 2620), while the other reported 19.1% (n = 177).

Associated social, psychological, and biological factors

Table 3 shows the various factors found to be associated with anxiety and depressive disorders. Sociodemographic factors associated with increased prevalence of anxiety and depressive disorders were female sex, middle age, and low level of education. Loss of husband (being widowed, separated, or divorced), increasing duration of marriage, and being a housewife were also positively associated. Women living in joint households with more than 12 members also showed a positive association; in contrast, one study reported a positive association for women living in unitary households. One study showed a positive significant association for relational problems with in-laws for women compared with other social problems. Chronic difficulties with housing, finances, and health were significantly associated with anxiety and depressive disorders. Absence of a confiding relationship was a significant factor in one study, as were lack of autonomy and arguments with husbands and in-laws in another. A disturbing event in the family was not significantly associated (P = 0.08).

Factors associated with risk of anxiety and depressive disorders in studies included in systematic review

Factors perceived by women to be associated with mental distress were low family income, marital disputes, too many children, and verbal abuse by in-laws. Studies that incorporated income found financial difficulties to be a significant factor, except for one study, in which the finding was just non-significant (P = 0.06).

What is the evidence for effectiveness of treatment or prevention in this population?

We could not find any prospective study of the natural course of the disorder or any rigorous controlled study addressing effectiveness of treatment and prevention. We found only one randomised controlled trial in mental health, regarding the ability of schoolchildren to detect mental disorders after having been given health education. 8

In our systematic review we found that socioeconomic adversity and relationship problems were major risk factors for anxiety and depressive disorders in Pakistan, whereas supportive family and friends may protect against development of these disorders.

Limitations of study

Our review may be subject to publication and selection bias as we were unable to systematically contact the experts in Pakistan for unpublished material or grey literature.

The coverage of the studies we identified is low. Despite detailed searches, we found that most studies satisfying our inclusion criteria were from the provinces of Punjab and Sindh, the two provinces with the largest population in Pakistan. The epidemiological data were collected from a handful of villages and urban settlements. There was considerable methodological variation in study design and in the instruments used. Thus one is unable to extrapolate these epidemiological findings to the whole of Pakistan.

Comparison with other low income countries

Using stringent criteria, Harding et al reported an overall frequency of anxiety and depression of 13.9% in four developing countries. 9 Community studies from Africa have reported prevalences of 24% in rural Uganda and 20%-24% in rural South Africa. Among patients attending primary care, the prevalence varied from 8% to 29%. Patients attending primary care in India showed prevalences between 21% and 57%. 1

In relation to risk factors, Abas and Broadhead found a significant association with formal employment, below average income, overcrowding, and certificate of secondary education in urban Zimbabwe. 10 In the same study, they also found a significant association with humiliation or entrapment and with death or other loss. 11 Bhagwanjee in rural South Africa found a significant association with age (risk increasing with age, to a maximum among people aged 30-39 years), single marital status, unemployment, low income, and low educational level. 12 Similar risk factors were found in studies from Pakistan. However, we found that the reported overall rates were higher in Pakistan and higher among rural than urban populations compared with the above studies. The question is whether these differences are an artefact of measurement or are because of specific factors operating in Pakistan.

Possible reasons for our findings

Pakistan's population has been exposed to sociopolitical instability, economic uncertainty, violence, regional conflict, and dislocation for at least the past three decades. 13 These are risk factors for psychiatric disorders 3 and may help explain the findings of this review.

As in many other countries, women in Pakistan generally have higher rates of illness than men. In a recent study, the main health problems reported by women were mental tension leading to headache and white vaginal discharge leading to body pains and fatigue. 14 In another study, most women perceived that financial, interpersonal, and family problems were causative or contributory factors in their ill health. They also linked their health to broader social institutions and cultural norms and expectations regarding women's roles and relationships between family members. 15

The need for stronger evidence and improved research capacity

The argument that health will automatically improve with economic growth is not supported by the current evidence. Diseases will not go away without specific investments in health interventions. 3 A coherent mental health policy with a strategic implementation plan is essential for countries that wish to enhance their social, economic, and social capital. 16

A major obstacle in formulating effective health policy is the lack of robust epidemiological research in Pakistan. 17 Our review highlights the absence of survey evidence and data from wider regions of Pakistan with regard to anxiety and depression, and the lack of outcome studies and prevention and treatment trials. The time is right for Pakistan to build on this research effort by increasing investment in research capacity. It would also be helpful to have a national epidemiological survey of mental disorders. Such surveys are useful to assess the needs of the population, document the use of existing services, obtain valid information on prevalence and associated risk factors, and monitor the health of the population and trends. 16

Available evidence suggests a major social cause for anxiety and depressive disorders in Pakistan, and an overall prevalence of 34%. This evidence is limited because of methodological problems. Nationally representative psychiatric morbidity surveys and controlled treatment trials are required to inform policy in order to control morbidity from anxiety and depressive disorders.

What is already known on this subject

Anxiety and depressive disorders are associated with considerable economic burden

These disorders represent an emerging public health threat in low income countries

What this study adds

In Pakistan relationship problems, financial difficulties, and low educational level are positively associated with anxiety and depressive disorders, whereas having a supportive relationship is negatively associated

Systematically collected, peer reviewed evidence suggests an overall prevalence of 34% for anxiety and depressive disorders in this population, but this finding must be treated with caution because of methodological limitations

Nationally representative psychiatric morbidity surveys and controlled treatment trials are needed to inform policy in order to control morbidity from anxiety and depressive disorders in Pakistan

Funding None.

Competing interests None declared.

Ethical approval Not required.

Contributors IM proposed the idea, which was further developed by RJ. IM performed the literature search and data extraction. IM and RJ both wrote the paper. IM is guarantor for the study.

Institute of Medicine
World Health Organization
Desjarlis R ,
Eisenberg L ,
Population Division, Department of Economic and Social Affairs, United Nations Secretariat. U.N.
Greenhalgh T
Mubbashar M ,
Harding TW ,
de Arango MV ,
Baltazar J ,
Climent CE ,
Ibrahim HH ,
Ladrido-Ignacio L ,
Broadhead J
Broadhead J ,
Bhagwanjee A ,
Petersen I ,
Winkvist A ,

SYSTEMATIC REVIEW article

Prevalence of depressive symptoms among university students in pakistan: a systematic review and meta-analysis.

$\nMuhammad Naeem Khan,$

1 Metro South Addiction and Mental Health Services, Brisbane, QLD, Australia
2 School of Medical Sciences, Griffith Health, Griffith University, Brisbane, QLD, Australia
3 School of Public Health, Global Health Institute, Xi'an Jiaotong University, Xi'an, China
4 Department of Psychiatry, King Edward Medical University, Lahore, Pakistan
5 Institute of Population Health, University of Liverpool, Liverpool, United Kingdom
6 Human Development Research Foundation (HDRF), Islamabad, Pakistan

Background: In Pakistan, almost 30% population is between 15 to 29 years of age, with university enrollment rates of 10–15%. Although there is a growing concern on mental health of university students across the globe, studies from low and middle income countries such as Pakistan are scarce. We conducted a systematic review and meta-analysis of prevalence of depressive symptoms among Pakistani university students.

Methods: PubMed, Web of Science, PsycInfo and Google Scholar were searched between 15 to 30th January 2020. Cross-sectional and longitudinal studies, published till 31st December 2019 were included. Data on study characteristics and prevalence of depressive symptoms were extracted. Meta-analysis was conducted using random effects models. To estimate subgroup difference based on study characteristics, meta-regression and sub-group analyses were conducted.

Results: In total, 26 studies involving 7,652 participants were included in review. Overall prevalence of depressive symptoms was 42.66% (95% CI: 34.82% to 50.89%), with significant heterogeneity among studies. Subgroup analyses revealed a significant difference in prevalence estimates based on depression screening instrument and study major. Statistically significant differences were observed among studies employing different psychometric scales (test for subgroup differences, Q = 21.92, p < 0.05) and between students from different study majors (test for subgroup differences, Q = 3.76, p = 0.05).

Conclusion: Our study found that overall prevalence of depressive symptoms among university students in Pakistan was 42.66%, however, findings should be interpreted with caution. Large scale epidemiological surveys using valid and reliable tools are needed to better estimate prevalence of depression among Pakistani university students.

Introduction

Depressive disorders are leading cause of disability worldwide ( 1 , 2 ). Studies suggest that most Common Mental Disorders (CMDs) have their first onset before the age of 24 ( 3 ). Anxiety and mood disorders are highly prevalent among young people aged 18–29 years. Almost 40% of young people experience their first episode of depression before the age of 20, with an average age of onset in the mid-20s ( 4 ). These years are most important for education, employment and social relationships.

Over the last decade, there has been growing interest in the mental health of university students. Globally, 24 to 34% university students experience depressive symptoms ( 5 – 9 ). Depressive disorders are one of the major causes of years lost due to disability (YLDs) and Disability Adjusted Life Years (DALYs) in young people ( 10 ). Occurrence of depression during the critical period of transition from adolescence to adulthood may have adverse effects, not only on development and academic functioning, but also on future employment and work productivity. Studies have shown that depression leads to early attrition from university and poor academic performance ( 11 – 13 ). Moreover, depression is associated with lower employment prospects and unstable employment in adulthood ( 14 ).

Pakistan-a Context

Pakistan is one of the youngest countries in the region, with almost 30% population between 15 to 29 years of age ( 15 ). In addition to having limited resources to invest in education and health, Pakistan has witnessed some major crises over the last two decades. The country was hit by a major earthquake in 2005 and heavy floods in 2010. A long wave of terrorism and militancy (2000–2014) did not even spare schools and colleges. More than 100 children were dead in a terrorists attack on Army public school Peshawar in 2014-the highest death tool in a single terrorist attack in the world. In 2016, a university in north-west province was attacked by terrorists, resulting in deaths of 19 students and teachers.

In Pakistan, a whole generation has grown up in an uncertain and insecure environment. Almost 70% population lives in rural areas. Meanwhile, over the last 20 years, trend of enrollment in higher education institutes has increased substantially with 10–15% of the eligible age group of 18–24 in universities or professional colleges ( 16 , 17 ). Even from less privileged areas, young people are getting higher education. Most of these people are form the first generation of their families to receive higher education.

With almost non-existent career counseling and mental health services at campuses, university students in Pakistan battle with a highly competitive environment, financial constraints, future uncertainty and parental and societal demands to excel in studies and secure good jobs. All these stressors put university students at high risk of developing common mental health problems particularly depression. For a developing country like Pakistan, health and well-being of its youth is of utmost importance as they are the future human capital.

There is a need for reliable estimates of prevalence of mental health problems among university students to design interventions tailored to specific needs of youth in Pakistan. Present study aims to conduct systematic review and meta-analysis on prevalence of depression among university students in Pakistan.

Study Design

This systematic review and meta-analysis was done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 18 ). A complete PRISMA Checklist is available as Supplementary Table 2 .

Participants, Intervention Comparators

Eligibility Criteria were defined according to the PICO ( 18 ).

Population - University students of any age.

Intervention/Exposure - Depression/depressive symptoms.

Intervention - not required for inclusion.

Comparison - not required for inclusion.

We included cross-sectional or longitudinal studies (baseline data) reporting prevalence of depressive symptoms among university students in Pakistan.

Exclusion criteria were studies reporting other study designs such as Randomized Controlled Trials (RCTs), case control studies, reviews (narrative and systematic), conference proceedings, case reports, qualitative studies, editorials, opinion papers, and letters. In addition, we did not include unpublished or non-peer reviewed articles.

Systematic Review Protocol

Protocol for this systematic review is registered in International prospective register of systematic reviews (PROSPERO) under registration number CRD42020170099.

Literature Search Strategy and Data Sources

We systematically searched PubMed, Web of Science, PsycInfo and Google scholar databases from January 15 th to 30 th 2020 for studies reporting primary data on depressive symptoms among university students in Pakistan, published till December 2019. In addition, the authors screened the reference lists of identified articles using the approaches implied by the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( 18 ). For the database searches, a pre-tested search strategy, combining terms related to university students and depression was employed. To avoid irrelevant results, search was restricted to only English language studies as no research studies are published in local/national language of Pakistan (Complete details of the search strategy appear in Supplementary Table 1 ).

Studies Selection and Data Extraction

The database searches were conducted by one author (MNK). After deletion of duplicate records using Endnote software, two authors (PA and MNK) independently screened all the titles and abstracts against the eligibility criteria. Any disagreements regarding inclusion for full-text screening were resolved through discussion with a third reviewer (SI). Thereafter, two authors (PA and MNK) independently reviewed the full-texts of all included articles. Disagreements were discussed with third author (SI) to achieve consensus. One author (PA) extracted data from all the included articles while 2nd author (MNK) extracted data from 25% of the studies to ensure accuracy and completeness of data extraction. Before starting the data extraction, both authors extracted data from three articles independently to establish inter-rater reliability. We found good inter-rater reliability between the two reviewers (k = 0.85).

Using a standardized data extraction sheet, data on following characteristics of included studies was extracted: author and publication years, study design, mean age of sample (or range, where mean was not available), sample size, sampling technique, number and percentage of females in the sample, education level, study major, instrument used to screen for depression, screening instrument cutoff, number of females with depression and overall prevalence of depressive symptoms.

Risk of Bias

Risk of bias in the included studies was assessed using a modified version of the Joanna Briggs Institute (JBI) critical appraisal checklist for prevalence studies ( 19 ). JBI is frequently used quality assessment tool for prevalence studies ( 20 – 22 ). This checklist assesses each study on 9 items including sample representativeness, recruitment appropriateness, adequate sample size, description of subjects and setting, valid ascertainment and measurement of the condition, thoroughness of reporting statistical analysis, standard measurement for all participants and adequacy of response rate. We modified Item 5 (original item “Was data analysis conducted with sufficient coverage of the identified sample” changed to “was scale valid/reliable in Pakistani context). Studies were categorized to be at low risk of bias (≥7 points), moderate risk of bias (4–6 points) or high risk of bias (<4 points). The quality assessment did not determine inclusion/exclusion of the study in meta-analysis.

Data Analysis

Descriptive statistics pertaining to prevalence of depressive symptoms and its overall severity were extracted. Studies were assessed based on methodological and statistical heterogeneity. Due to significant heterogeneity, data was pooled using random effects model and forest plots were generated displaying pooled prevalence with 95% confidence intervals. Between-study heterogeneity was assessed using standard χ 2 tests, Tau 2 and the I 2 statistics ( 23 , 24 ). I 2 was presented as the percentage of variability in prevalence estimates due to heterogeneity rather than sampling error, or chance, with values ≥75% indicating considerable heterogeneity ( 23 , 24 ). Sensitivity analysis using single study “knock out” approach was used to determine influence of each study on the pooled prevalence.

Publication bias was assessed by visual inspection of the funnel plot and Egger's tests (considered significant at p < 0.1). ( 25 , 26 ). Duval and Tweedie's trim and fill method was used to adjust pooled prevalence estimate for publication bias ( 27 ). To explore heterogeneity among studies, we conducted subgroup analyses for categorical moderators, and meta-regression for continuous variables. Subgroups were conducted by field of study, level of education, university type (public /private), depression screening tool, sampling technique and study quality. All subgroup analyses were conducted using the mixed-effect method where a p -value of 0.05 was considered as having statistically significant subgroup differences.

Meta regression with maximum likelihood method and random effects was conducted to determine effect of age, sample size and percentage of females in the sample on the pooled prevalence. To ensure appropriate statistical power, we conducted subgroup analysis when subgroups were reported in at least four studies ( 28 ). While meta-regression analysis were run for moderators reported in at least ten studies ( 29 ). All the analysis were conducted in Comprehensive Meta-analysis software (CMA) version 3 ( 30 ). All statistical tests were 2-sided and p -values < 0.05 was considered statistically significant.

Study Selection Process

Our databases search yielded 137 records. After removal of 32 duplicates, 105 studies were screened for titles and abstracts against inclusion and exclusion criteria. After the screening process, a total of 45 full texts were found eligible for further assessment. We excluded 19 studies as the studies did not report prevalence of depression. A total of 26 full-texts were included in both the qualitative and quantitative synthesis. A detailed flow chart of the search and selection process is presented in Figure 1 .

Figure 1 . PRISMA flow diagram.

Characteristics of Included Studies

Table 1 summarizes the basic characteristics of included studies. In total, 26 studies involving 7,652 participants were included in the analysis. The median number of participants per study was 289 (range, 66–1000). No longitudinal study was identified and all the included studies in our review were cross-sectional studies. Majority of the studies (24/26, 92%), were conducted with undergraduate students and only two studies included both undergraduate and graduate students. More than half (17/26, 65%) studies included only medical students. Among the studies conducted with students from non-medical majors, studies did not explicitly mentioned the study discipline. 10 (38.50%) studies recruited sample from public universities, 6 from private universities, 5 studies included mix sample from both, private and public universities, and 5 studies did not specify university type. Most of the studies used self-reporting screening tools to assess depression; 4 studies (15%) used Hospital Anxiety and Depression Scale (HADS), 3 studies (11.54%) used Beck Depression Inventory (BDI), 4 studies (15%) used Depression Anxiety Stress Scale-21 (DASS-21), 3 studies (11.54%) used Depression Anxiety Stress Scale-42 (DASS-42), Beck Depression Inventory-II (BDI-II), Center for Epidemiological Studies Scale for Depression (CESD) and Zungs Self-report Depression Scale (Zung-SDS) were used in two studies each. One study each used Quick Inventory for Depression Screen (QIDS), Patient Health Questionnaire-9 (PHQ-9), Duke Health Profile and Hamilton Depression Scale (HAM-D) while 3 studies did not specify the depression ascertainment methods.

Table 1 . Characteristics of included studies.

Synthesized Findings

Prevalence of depression in university students in pakistan.

There was an evidence of substantial statistical heterogeneity among the included studies (I 2 = 97.68%, Cochran's Q = 1078.55, p < 0.001). Therefore, random effects were employed while pooling event rates across studies, yielding a pooled prevalence rate of 42.66% (95% CI: 34.82 to 50.89%) (see Figure 2 ). Out of 7,652 university students, a total of 3,549 reported having depressive symptoms according to different screening tools.

Figure 2 . Meta-analysis of 26 studies on prevalence of depressive symptoms among university students in Pakistan.

Subgroup Analyses

Several subgroup analyses were conducted in this meta-analytical investigation. Prevalence of depression among undergraduate students ( n = 24) was slightly lower as compared to studies that included sample from both graduate and undergraduate levels ( n = 2). Among undergraduates students a prevalence rate of 42.24% (95%CI: 33.5-49.79%) of depressive symptoms was reported as compared to 48.86% (95% CI:2.88-96.85%) by other student population. Studies employing random sampling yielded lower prevalence rates (33.47%, 95% CI: 23.26-45.51%) than non-random counterparts (44.49%, 95% CI: 35.29-54.09%). Studies with lowest risk of bias reported the lowest prevalence rate of 30% (95% CI: 31.13-51.37%) than their counterparts with moderate (48.09%, 95% CI: 36.53-59.86%) and highest risk of bias (40.71%, 95% CI: 14.82-51.37%), however, none of these difference was statistically significant ( Table 2A ).

Table 2A . Subgroup analysis based on study characteristics.

Students enrolled in disciplines other than medicine reported higher prevalence of depressive symptoms (53.59%, 95% CI: 40.71%-66%) as compared to medical students (36.90%, 95% CI: 27.14-47.86%). The difference was statistically significant (test for subgroup differences, Q = 3.76, p = 0.05). Lowest percentage of depressive symptoms were reported by private sector university students (26.13%; 95% CI: 14.37-42.71%) than those studying in public (government funded) universities (42.60%, 95% CI: 29.45-56.90) or studies which included sample from both public and private universities (45.94% 95%CI: 27.31-65.77%). However, this difference did not yield statistical significance (test for subgroup differences, Q = 3.13, p = 0.21) (see Table 2A ).

When comparing prevalence rates of depression between studies employing different psychometric scales, statistically significant differences were observed (test for subgroup differences, Q = 21.92, p < 0.05). There was evidence of significant variation in the extent of heterogeneity observed across studies employing different scales. Lowest prevalence of depressive symptoms was reported as per BDI-II scale and the highest according to CES-D and HAM-D scale (see Table 2B ).

Table 2B . Subgroup analysis based on depression screening instrument.

Meta Regression Analysis

Meta-regression analyses using random effects model was conducted to analyze association between prevalence rates of depressive symptoms, age of sample, total sample size and proportion of females in the sample. Each variable accounted for only 3% of variance in heterogeneity in the reported effect size, and did not yield statistical significance ( p > 0.05). (see Tables 3A – C ).

Table 3A . Meta-regression analysis for the prevalence (%) of depression in university students with proportion of females.

Table 3B . Meta-Regression analysis for the prevalence (%) of depression in university students with mean age of sample.

Table 3C . Meta-regression analysis for the prevalence (%) of depression in university students with sample size.

Sensitivity Analysis

Sensitivity analysis did not indicate any changes in the mean prevalence when individual studies were removed from the meta-analysis, except the removal of two studies ( 34 , 42 ) independently reduced the prevalence rate of depression from 42.7 to 40%. (See Supplementary Figure 1 ).

Assessments of Publication Bias

There was some evidence of publication bias in reporting of prevalence of depression among university students (Egger's statistic = −6.09 (3.44), p = 0.09).

Trim and fill method using random effects was used to adjust the pooled prevalence estimates for publication bias. After imputing one study to the right of mean, it yielded an adjusted prevalence of 40.45% among university students (95% CI: 31.21% to 50.42%) (see Figure 3 ).

Figure 3 . Funnel plot for publication bias with trim and fill method.

Most of the included studies had a moderate to high risk of bias. Mean quality score was 5.12 (SD; 1.53) out of 9. Only 6 studies had low risk of bias, while 15 out of 26 (58%) studies had a moderate to high risk of bias. Out of 26 studies, 21 studies did not report or cite the reference of scale's psychometric properties for Pakistani population. Only 4 (15%) studies employed random sampling technique, and 11 (42%) studies included sample from multiple schools/universities. Response rate was given in 12 (46%) studies. Risk of bias score for all individual studies has been shown in Table 4 .

Table 4 . Risk of bias in included studies.

Summary of Main Findings

In this systematic review and meta-analysis of 26 studies involving 7652 university students, prevalence of depressive symptoms was found to be 42.66% (95% CI: 34.8-50.9%). Overall prevalence is higher than the recent estimated prevalence rates of 24% (95% CI, 19.2%−30.5%) among university students in LMICs as reported by Akhtar et al. ( 9 ) as well as recent global estimates among medical students (27%, 95% CI, 24.7 to 29.9%) reported by Rotenstein et al. ( 7 ). This is alarming given relatively low university enrollments rates in low resource countries like Pakistan.

University environment in Pakistan is getting more and more competitive. University teachers, parents and society in general value high achievers. The constant pressure of getting good grades and landing a decent job may lead to feeling of stress and depression. There are no psychological and career counseling services at university campuses. Very few available metal health services are concentrated in tertiary healthcare facilities in big cities. In addition, lack of awareness and training among teachers to recognize and support students with common mental health problems and stigma attached to mental health problems are major barriers in seeking professional help. All these factors cause unnecessary delay in treatment, resulting in worsening the problems.

Prevalence of depression among students with non-medical majors was significantly higher than those with medical. Those enrolled in medicine reported lower prevalence of depression (36.90%, 95% CIs: 27.14-47.86%) than those in degree programs other than medicine (53.59%, 95% CI: 40.71-66%). In Pakistan, medicine and engineering are the first choice of most of students and their parents. However, securing admission in these fields is very competitive due to limited number of public medical and engineering colleges. Many students who cannot make to medical and engineering colleges, choose other fields. At one hand, they may feel less satisfied and not being able to fulfill the expectations of parents, and frustrated with highly competitive job market and limited career opportunities on the other hand. However, it should be noted that there were very few studies having sample from non-medical study majors in this review.

Significant difference in prevalence estimates was found in studies using different screening tools. Different tools employ different cut-offs and sometimes same tool can be used with different cut-offs. Moreover, most of studies did not mention the psychometric properties for Pakistan population. Previous studies also indicate a difference in prevalence estimates based on screening instruments ( 7 ).

Quality assessment of studies indicated few high quality studies. Only few studies employed random sampling techniques and recruited sample from multiple school, this could have introduce a selection bias in the individual studies included in this review, indicating scarcity of large scale, valid and reliable surveys.

Moreover, we found only 26 studies in four major databases, without publication dates restrictions. This is an indication of overall scarcity of research in this field in Pakistan

A high prevalence of depressive symptoms among Pakistani university students is a threat to healthy development of students and their smooth transition to adulthood. It may have long-term adverse effects for individuals as well the nation. Researchers and policy maker should focus this problem in future research. There is need for valid and reliable estimates prevalence of depressive symptoms among Pakistani university students, following guidelines for large epidemiological studies ( 57 ). Longitudinal studies are needed to analyze risk and protective factors for depression, with a focus on cultural factors. Barriers to access to mental health services need to be addressed by campus-based mental health services and community based interventions to reduce stigma associated with mental health problems. Due to the socio-political situation in general and in the specific context of COVID-19 outbreak, there is a need to integrate psychological wellbeing strategies in the university curricula. This will help students to combat the adversities they are constantly exposed to as well as serve as a solution to scarcity of specialized and community-based mental health services. Teachers training in identification and recognition of common mental health disorders, and basic counseling skills can also be integrated in usual teachers training.

Strengths and Limitations

This is the first study to systematically review the prevalence of depression among university students in Pakistan. We conducted meta-analysis to summarize prevalence estimates. We did not apply any restrictions on publication date to include as many studies as possible.

Our findings should be interpreted under the light of a few limitations. Studies included in this review used variety of screening tools, different sample sizes and screening tools cut-offs, that introduced substantial heterogeneity. Depression ascertainment methods employed by the most of studies in our systematic review were self-reporting screening tools. These tools do not provide clinical diagnosis. Most of studies did not report the psychometric properties for Pakistani population. We did not included gray literature such as non-published or non-peer reviewed studies in our meta-analysis, which may have introduced publication bias in present results. One more limitation of the current review is that we did not include any social factors for depression or co-morbidities in our analyses.

In this systematic review and meta-analysis, prevalence of depressive symptoms among Pakistani university students was found to be 42.66% with a huge variation among studies, however, there were very few good quality studies. Future research efforts should be directed to conduct large epidemiological studies for valid and reliable estimates of depression and to implement interventions to prevent and treat depression among university students.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Author Contributions

MNK and PA conceptualized and designed the study. PA, SI, and MNK performed the article search and data extraction. AW and MNK analyzed the data. AW, MNK, and PA interpreted the results. MNK and PA drafted the manuscript in support with AW and SI. All authors reviewed and approved the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer SS declared a shared affiliation, though no other collaboration, with one of the authors AW to the handling Editor.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2020.603357/full#supplementary-material

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Keywords: depression, university students, Pakistan, systematic review, meta-analysis (as topic), low resource setting

Citation: Khan MN, Akhtar P, Ijaz S and Waqas A (2021) Prevalence of Depressive Symptoms Among University Students in Pakistan: A Systematic Review and Meta-Analysis. Front. Public Health 8:603357. doi: 10.3389/fpubh.2020.603357

Received: 06 September 2020; Accepted: 30 November 2020; Published: 08 January 2021.

Reviewed by:

Copyright © 2021 Khan, Akhtar, Ijaz and Waqas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ahmed Waqas, ahmed.waqas@liverpool.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Depression and social stress in Pakistan

Affiliation.

1 Institute of Psychiatry, Rawalpindi General Hospital, Pakistan.
PMID: 10824659
DOI: 10.1017/s0033291700001707

Background: The high prevalence of depression in developing countries is not well understood. This study aimed to replicate the previous finding of a high prevalence of depression in Pakistan and assess in detail the associated social difficulties.

Method: A two-phase survey of a general population sample in a Pakistani village was performed. The first-phase screen used the Personal Health Questionnaire (PHQ) and the self-rating questionnaire (SRQ). A one in two sample of high scorers and a one in three sample of the low scorers were interviewed using the Psychiatric Assessment Schedule (PAS) and Life Events and Difficulties Schedule (LEDS).

Results: A total of 259 people were screened (96% response rate). The second stage yielded 55 cases, of whom 54 had depressive disorder, and 48 non-cases. The adjusted prevalence of depressive disorders was 44-4% (95% CI 35.3 to 53.6): 25.5% in males and 57.5% in females. Nearly all cases had lasted longer than 1 year. Comparison of the cases and non-cases indicated that cases were less well educated, had more children and experienced more marked, independent chronic difficulties. Multivariate analysis indicated that severe financial and housing difficulties, large number of children and low educational level were particularly closely associated with depression.

Conclusion: This study confirms the high prevalence of depressive disorders in Pakistan and suggests that this may be higher than other developing countries because of the high proportion of the population who experience social adversity.

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Prevalence of depression and anxiety among general population in Pakistan during COVID-19 lockdown: An online-survey

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Published: 08 February 2022
Volume 43 , pages 8338–8345, ( 2024 )

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Irfan Ullah ORCID: orcid.org/0000-0003-1100-101X 1 ,
Sajjad Ali ORCID: orcid.org/0000-0002-8024-5942 2 ,
Farzana Ashraf ORCID: orcid.org/0000-0003-0110-2618 3 ,
Yasir Hakim 1 ,
Iftikhar Ali 4 ,
Arslan Rahat Ullah 5 ,
Vijay Kumar Chattu ORCID: orcid.org/0000-0001-9840-8335 6 , 7 , 8 &
Amir H. Pakpour ORCID: orcid.org/0000-0002-8798-5345 9

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The present study's aim is to find the prevalence of two of the common indicators of mental health - depression and anxiety – and any correlation with socio-demographic indicators in the Pakistani population during the lockdown from 5 May to 25 July 2020. A cross-sectional survey was conducted using an online questionnaire sent to volunteer participants. A total of 1047 participants over 18 were recruited through convenience sampling. The survey targeted depression and anxiety levels, which were measured using a 14 item self-reporting Hospital Anxiety and Depression Scale (HADS). Out of the total sample population ( N =354), 39.9% suffered from depression and 57.7% from anxiety. Binary logistical regressions indicated significant predictive associations of gender ( OR=1.410 ), education ( OR=9.311 ), residence ( OR=0.370 ), household income ( OR=0.579 ), previous psychiatric problems ( OR=1.671 ), and previous psychiatric medication (OR=2.641) . These were the key factors e associated with a significant increase in depression. Increases in anxiety levels were significantly linked to gender ( OR=2.427 ), residence ( OR=0.619 ), previous psychiatric problems ( OR=1.166 ), and previous psychiatric medication ( OR=7.330 ). These results suggest depression and anxiety were prevalent among the Pakistani population during the lockdown. Along with other measures to contain the spread of COVID-19, citizens' mental health needs the Pakistani government's urgent attention as well as that of mental health experts. Further large-scale, such as healthcare practitioners, should be undertaken to identify other mental health indicators that need to be monitored.

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Introduction

In early December 2019, many cases of pneumonia caused by a novel beta coronavirus (the 2019 novel coronavirus) were identified in Wuhan, the capital city of Hubei, China (Guan et al., 2020 ). This virus has been named severe acute respiratory syndrome coronavirus2 (SARS-CoV-2), which displays phylogenetically identical characteristics to severe/acute respiratory syndrome coronavirus (SARS-Co-V) (Lu et al., 2020 ). The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020, when the registered cases of COVID-19 reached 118,000, and the number of deaths reached 4291 in 114 countries (WHO, 2020 ). Amid epidemics, there is a growing sense of fear among individuals of becoming infected with viral diseases, which further causes anxiety and depression (Ahorsu et al., 2020 ; Hall et al., 2008 ). Anxiety is defined as the body's normal response to stress (Holland, 2018 ). Depression, on the other hand, is defined as a lack of interest in everyday tasks. It is hypothesized that persons who are exposed to a pandemic without immunization will experience anxiety, tension, and depression as a result of their fear of the unknown (in this case, the coronavirus) (Lin et al., 2021 ).

Since the outbreak of COVID-19, a large number of studies have been conducted on people’s mental health during lockdown and quarantine situations, particularly on ways to cope with the spread of these conditions. All the research has led to the conclusion that various restrictions to an individual’s behavior can have an adverse effect on their mental health. For example, a study conducted by Sprang and Silman ( 2013 ) showed that 25% of isolated parents and 30% of isolated children had symptoms of post-traumatic stress disorder (PTSD). A Korean by Jeong et al. in 2016 found that 7.6% of patients displayed symptoms of anxiety during the epidemic of Middle Eastern Respiratory Syndrome (MERS). In addition, similar results were reported in Canada during the 2003 severe acute respiratory syndrome (SARS) outbreak (Reynolds et al., 2008 ).

Fear, anxiety, desperation, and helplessness can be associated with epidemic outbreaks of infectious diseases, especially when infection and death rates are reported to be high (Ashraf et al., 2021 ; Rajabimajd et al., 2021 ). Tension spikes in the general population during infectious disease outbreaks, bringing not only poor mental health but also major economic consequences in the social and household sectors (Smith et al., 2019 ). The travel bans in China during the outbreak of SARS in 2003 and avian influenza in 2013 had a huge impact on the business sector and the jobs of individuals (Wishnick, 2010 ). The mental health of the general population can be affected by the COVID 19 pandemic and is of great importance from various aspects (Xiang et al., 2020 ). Due attention needs to be paid to the psychological traumas, and mental health problems experience in the general population. Controlling situations such as lockdowns can provoke anxiety responses and increase the fear and prejudice against infected and affected persons (Person et al., 2004 ). Studies examining the effect of COVID-19 on mental wellbeing not only highlight problematic areas but can also generate ways to provide health care services with the necessary information and support to deliver mental health treatment to those in need. Though a large number of studies have explored mental health in the particular context of mental health outcomes, yet there is a paucity of research assessing the prevalence of anxiety and depression symptoms relating to socio-demographic factors. Furthermore, the present study will be a valuable addition to the existing literature on the cultural aspect of mental health and depression.

Methodology

Study design and participants.

A cross-sectional study was conducted from 5 May to 25 July 2020 in Pakistan, targeting the general population. Online survey was accompanied by a self-administered questionnaire. The online survey was distributed by commonly used social media such as Facebook, WhatsApp and Telegram. Participants were also asked to share the online survey with their peers to obtain a more normal distribution and representative sample. To control the possible confounding factors, certain inclusion criteria were devised. Participants had to be (i) Pakistani nationals residing in the country since the outbreak of the corona pandemic, (ii) at least 18 years of age, (iii) able to speak Urdu as their first language, and (iv) not previously diagnosed with a psychological or psychiatric disorder or on any medications for the same. The exclusion criteria include: (i) Non- Pakistani nationals inside the country as well as Pakistani national living abroad, (2) anyone less than 18 years of age, (iii) anyone with a prior diagnosis of depression or anxiety disorder or any other mental health issue (iv) anyone who is on anti-psychotic or psychiatric medications. Dropouts and participants who provided insufficient or incomplete data were excluded from the study. The final sample comprised 1047 participants recruited through convenience sampling. The study was approved by the ethics committee of COMSATS University Lahore (REF: CUI/LHR/HUM/178) and carried out in accordance with the human research ethics outlined in the Helsinki Declaration 1975. The online survey comprised three sections; informed consent, demographic information, and study tools. Informed consent was provided by all the participants before completing the online survey. The participants were assured that their participation in the study was voluntary and were free to withdraw from the survey at any point without any privacy concerns. The survey remained anonymous to assure the reliability, replicability and confidentiality of the data

Demographic Questionnaire: With the help of a self-reporting standard questionnaire, socio-demographics parameters of the participants such as age, gender, marital status, education, region, area of residence, occupation, monthly household income, and smoker status were collected.

Hospital Anxiety and Depression Scale (HADS): The HADS was used to assess anxiety and depression in the study sample (Waqas et al., 2019 ). HADS is a valid measure for assessing mental health outcomes in terms of depression and anxiety and is widely used locally and internationally. The scale comprises 14 items equally distributed to assess anxiety (e.g., “I feel tense or wound up” ) and depression (e.g., “ I still enjoy the things I used to enjoy ”) through responses to statements. Two of the items are reverse coded (items 7 and 10) to cross-check the random responses. Each item is rated on a four-point Likert scale (0 to 3 with diverse descriptions for each item) with total scores ranging from 0 to 21. High scores are an indicator of a high level of depression and anxiety. Scores on HADS can be used on a continuum and as categorical as well (e.g., normal=0-7; mild=8-10; moderate=15-21 and severe=15-21). The present study showed a good fit for the alpha coefficient for total HADS (α=.85), depression (α=.72) as well as anxiety (α=.84) subscales.

Data Analysis

The data were analyzed using IBM SPSS Statistics V.26.0. All the data were coded in SPSS, and invalid data (e.g., random responses, incomplete responses, and repetitive responses) were dealt with using missing values analysis and outliers’ analysis in SPSS. We ran descriptive statistics -means, standard deviation, percentages, and frequency distribution - to estimate the descriptive characteristics of the study variables. First, the association between independent and dependent variables was determined using the Chi-square test of association. In addition, we ran logistical regression analyses to evaluate the degree of association of socio-demographic characteristics with depression and/or anxiety. The level of significance had a p-value < 0.05 and a confidence interval (CI) of 95%.

Of the total 1047 participants, a majority 550 (52.5%) were females and 497 (47.5%) were males. The vast majority (85%) of them were aged 18 - 30 years and the remaining (15%) were over 30 years of age indicating that the majority of the sample comprised of young adult population. The mean age (S.D) was found to be 25.76 ± 11.262 years which also indicates the higher use of social media platforms by this age groups. The participants resided in all provinces: 53.7% lived in Sindh, 22.5% in Punjab, 12.9% in Khayber Pukhtoonkhawah, 10.4% in Islamabad, 0.3% in Azad Jammu Kashmir, 0.2% in Gilgit Baltistan, and 0.1% Balochistan. More than 1/3 of the study participants (77.8%) were unmarried. Only 221(21.1%) were married, 6 (0.5%) were separated/divorced and 5 (0.4%) widowed (see Table 1 ).

Gender was found to be significantly associated with depression ( p<0.01 ) and anxiety (p=.001) . Education status was only significantly associated with depression ( p<0.001 ). Place of residence and occupation were significantly associated with both depression ( p<0.001 ) and anxiety ( p< 0.001 ). Household income was significantly associated only with symptoms of depression ( p<0.01 ). Previous psychiatric illness and previous psychiatric medications were significantly associated with depression ( p<0.001 ) and anxiety ( p<0.001 ) (Table 2 ).

Binary logistic regressions were performed to determine any predictive association of socio-demographics with depression and/or anxiety. The analysis indicated that gender, education, residence, household income, previous psychiatric problems and previous psychiatric medication are the key factors associated with a significant increase in depression among the participants with odds ratios of 1.410 [1.099-1.809], 9.311 [1.020-85.030], 0.370 [0.229-0.596], 0.579 [0.227-1.480], 1.671 [1.244-2.246], 2.641 [1.748-3.989] and 4.711 [2.416-9.187], respectively. In addition, gender, place of residence, previous psychiatric problem, and previous psychiatric medications were found to be the key factors associated with a significant increase in depression with an odds ratio of 2.427 [1.888-3.119], 0.619 [0.376-1.019], 1.166 [0.458-2.969], 7.330 [3.876-13.863] and 5.313 [2.236-12.629], respectively (Table 3 and 4 ).

Out of the total sample population, 39.9% suffered from depression and 57.7% from anxiety (Table 5 ).

The present study indicated the significant prevalence of anxiety and depression in a sample of the general population of Pakistan during the COVID 19 outbreak from 5 May to 25 July 2020. Our study findings suggest that being a woman with a lower level of education, living in an urban area, occupation, previous psychiatric illness, and medication were significantly associated with symptoms of anxiety and depression. Our study findings suggest that women were more likely to be anxious and depressed (67.8% & 43.8%, respectively) than males (46.5% and 35.6%, respectively) during the lockdown. This result is supported by a study conducted by (Farooq et al., 2019 ) in which females were 2.5 times as anxious and depressed as males (39.4% vs. 23.3%, respectively). Another research (Zahidie & Jamali, 2013 ) found that the prevalence of anxiety and depressive symptoms were 29% and 66% among women, compared to 10% and 33% among men. These findings are backed by studies conducted globally which report higher anxiety symptoms among females in China (Zhou et al., 2020 ; Hou et al., 2020 ), India (Varshney et al., 2020 ), Oman (Badahdah et al., 2020 ) and Spain (González-Sanguino et al., 2020 ). Plausible reasons for the higher prevalence of anxiety and depression among women could be biological factors, socioeconomic disadvantage, loss of social status, maladapted coping strategies, and the lack of a support system for women in this country (Mirza & Jenkins, 2004 ). Other well-known reasoning may be that most women have to balance their household work and professional workload due to the inherited socio-cultural norms that still prevail in Pakistani households. Males are barely involved in household activities. As men spend more time at home due to the ‘stay home, stay safe’ policy of the government, the workload burden of the women in the household increases.

Moreover, anxiety and depression can be seen as more prevalent in urban and semi-urban locations. This could be because COVID-19 is more prevalent in urban settlements. Lockdown has had a great impact on all the densely populated cities of Pakistan, putting all the lives of the people living there on hold. Anxiety and depression can be significantly associated with the employment status of the general public, a local reflection of the hundreds of thousands of jobs being lost across the world. Pakistan’s Ministry of Finance revealed 3 million jobs had been lost during the COVD19 outbreak (Gulf news, 2020 ). The findings are supported by a Chinese study which showed that the prevalence of psychological health problems are more common among urban residents due to a great number of COVID-19 cases among cities and urban areas acting as epicenters of the diseases (Liu et al., 2021 ). Salary cuts and reductions in new jobs are expected. Moreover, uncertainty and possibly fear could have led to the development of more depression and anxiety symptoms. In addition, offices have been shut down because of the travel ban, and most employees are working from home. Lack of contact with co-workers could affect workers' motivation, satisfaction with work, and productivity. Not being able to meet deadlines and targets due to only earning hand of house and the usual pressure may cause a rise in anxiety levels among them.

Furthermore, household income is significantly associated with depression in the participants. The lower the household income, the more indicators of depressive symptoms there were. Sareen et al. ( 2011 ) found that low levels of household income are associated with mental disorders and suicide attempts, and a reduction in household income is associated with an increased risk of mental disorder incidents. One possible explanation for this could be that the lockdown imposed has disrupted the economic flow throughout the country.

Our results suggest that there is an increased prevalence of depression and anxiety among people with previous mental health problems and/or who were on medication for psychiatric disorders (p<0.001). One of the reasons for this is that the widespread lockdown has meant psychiatric patients are unable to contact their doctors in times of need. The closure of all psychiatric OPDs (Dawn news, 2020 ) and the current chaotic situation due to COVID-19 has increased the severity of some patients’ psychiatric conditions, leading to a spike in depression and anxiety among them. Also, being unable to travel and get medications during the lockdown is a reason for the spike.

Monitoring the mental health of populations during a pandemic is crucial, as public fear and fear induced by over-reacting behavior could act as a barrier to the control of infectious diseases (Dong & Bouey, 2020 ). In addition, the existing stringent lockdown measures and the uncertain period of home isolation reflect an ongoing traumatic occurrence that could potentially contribute to substantial long-term health costs. Therefore, epidemiological monitoring and targeted intervention should be introduced in good time to avoid more mental health issues in the future.

Limitations of the Study

Along with the strong evidence this study has highlighted, there are some limitations and bias which are common with any cross-sectional study. Firstly, the researchers did not go into the field to collect data but used electronic means during the lockdown since the public health regulations were in place. Since the participation was voluntary, only the young adults who were active on social media had more participation resulting in lack of representation of all socio-demographic features. Therefore, the results cannot be generalized to the entire population of Pakistan. Secondly, most of the study sample was from 18 to 30 years of age leaving the gaps for the middle aged and older age groups. Thirdly, considering the high illiteracy rate of Pakistan, our sample size was mostly made up of a literal population. All of these factors mean the results are not generalizable as representative of the entire population. They do, however, offer important indicators, likely to be shared more generally,

The present study reported the high prevalence of depression and anxiety in participants from a broad spectrum of the general population of Pakistan during the country-wide lockdown due to the COVID-19 pandemic. Mental Health Ordinance 2001 in Pakistan preserves the rights of citizens dealing with mental health issues and guarantees to take care of them. In these difficult times, the government of Pakistan should make mental health care one of its top priorities. Pakistan's government is doing its best to reduce the spread of the pandemic in the country; however, effective steps should also be taken for the care of mental health of its citizens.

Availability of data and materials

The data set is available upon request from the corresponding author.

Abbreviations

Coronavirus outbreak

Hospital Anxiety and Depression Scale

Severe acute respiratory syndrome coronavirus2

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Kabir Medical College, Gandhara University, Peshawar, Pakistan

Irfan Ullah & Yasir Hakim

Ziauddin Medical University, Karachi, Pakistan

Department of Humanities, COMSATS University, Lahore, Pakistan

Farzana Ashraf

Paraplegic Centre, Hayatabad, Peshawar, Pakistan

Iftikhar Ali

Department of Medicine, Northwest general hospital and Research Centre, Peshawar, Pakistan

Arslan Rahat Ullah

Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada

Vijay Kumar Chattu

Center for Transdisciplinary Research, SIMATS, Saveetha University, Chennai, India

Department of Community Medicine, Faculty of Medicine, Datta Meghe Institute of Medical Sciences, Wardha, India

Department of Nursing, School of Health and Welfare, Jönköping University, Gjuterigatan 5, 553 18, Jönköping, Sweden

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Contributions

IU and SA have made substantial contributions to the conception and design. FA, YH, IA, and ARU participated in the study design and data acquisition. IU, SA, VKC and AHP were involved in drafting and revising the manuscript. VKC provided critical comments and re-edicted the final draft. All authors have read and approved the manuscript.

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Correspondence to Amir H. Pakpour .

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Ullah, I., Ali, S., Ashraf, F. et al. Prevalence of depression and anxiety among general population in Pakistan during COVID-19 lockdown: An online-survey. Curr Psychol 43 , 8338–8345 (2024). https://doi.org/10.1007/s12144-022-02815-7

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DOI : https://doi.org/10.1007/s12144-022-02815-7

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> Journals
> The British Journal of Psychiatry
> Volume 193 Issue 5
> Case–control study of suicide in Karachi, Pakistan

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Case–control study of suicide in karachi, pakistan.

Published online by Cambridge University Press: 02 January 2018

Supplementary materials
eLetters (2)

In recent years suicide has become a major public health problem in Pakistan.

To identify major risk factors associated with suicides in Karachi, Pakistan.

A matched case–control psychological autopsy study. Interviews were conducted for 100 consecutive suicides, which were matched for age, gender and area of residence with 100 living controls.

Both univariate analysis and conditional logistic regression model results indicate that predictors of suicides in Pakistan are psychiatric disorders (especially depression), marital status (being married), unemployment, and negative and stressful life events. Only a few individuals were receiving treatment at the time of suicide. None of the victims had been in contact with a health professional in the month before suicide.

Suicide in Pakistan is strongly associated with depression, which is under-recognised and under-treated. The absence of an effective primary healthcare system in which mental health could be integrated poses unique challenges for suicide prevention in Pakistan.

About a million people die by suicide each year worldwide. Reference Bertolote and Fleischmann 1 Most research information on suicide comes from high-income countries. Few countries outside the Western world report suicide data to the World Health Organization regularly. 2 Prominent among those that do not report are most of the 57 countries with a majority Muslim population, including those with populations in excess of 100 million people, namely Indonesia, Pakistan and Bangladesh. Reference Khan 3

Pakistan is the sixth most populous country in the world (population 162 million). 4 Overall, 97% of the population are Muslims, 65% people live in rural areas and a third are below the poverty line. The literacy rate is around 35–40%. Official unemployment stands at 12% of the eligible workforce. Health spending is less than 1% of the annual budget; mental health does not have a separate budget.

Karachi is the country's most heavily populated city. In the last official census of 1998, the population was 9.339 million; 4 in 2003 it was estimated to be over 13 million. 5 The city is divided into 18 ‘towns’, each with a town police office. Each town police office has four to five police stations. When a suicide takes place, it is recorded in the police station and information sent to the town police office, which in turn forwards the report to the police headquarters, where a record of all suicides is kept.

Suicide is an under-studied and under-researched subject in Pakistan. Basic epidemiological data, for example, on national rates are not known. A variety of social, legal and religious factors make accurate reporting and data collection difficult. Reference Khan and Prince 6

From religious and sociocultural perspectives, suicide in Pakistan is viewed as a shameful act to be concealed. Families are often ostracised and there are implications, for example, for the marriage prospects of girls of the family. Consequently, numbers of attempted/completed suicide may be underestimated in Pakistan. Despite this, there is evidence that suicides have increased over the past few years. Reference Khan and Prince 6 – 8

We conducted a study to identify factors associated with suicide in Karachi, Pakistan. This has not been studied previously.

Study design

The study was conducted between January 2003 and December 2003. This was a pair-matched case–control study to explore relationships between exposures (socio-demographic factors, life-events, mental illness, etc.) and outcome (suicide).

Sample size calculations were based on two risk factors: life-events and difficulties, and depression. Estimating from previous Pakistani population-based research that life-events and difficulties will be present in 60% of controls, Reference Husain, Creed and Tomenson 9 with an odds ratio (OR) (suicide victims v . controls) equal to 3, 80% power and 5% significance level, a sample of 73 suicides and 73 controls was calculated. Similarly, taking a 30–40% prevalence of depression in controls Reference Mumford, Minhas, Akhtar, Akhter and Mubbashar 10 , Reference Mirza and Jenkins 11 with an OR of 3, 80% power and 5% significance level, the sample size came out to be 62 suicides and 62 controls. A sample size of 100 suicides and 100 controls was taken to adjust for confounding variables.

Selection of suicides and controls

We studied the first 100 consecutive suicides during the study period. The Karachi police provided information on suicides.

Controls were matched to suicide victims with respect to age (±2 years), gender and area of residence. They were identified in the immediate vicinity of 20 houses in the same street. When a suitable control could not be identified in the same street, the next streets were visited until a suitable control was identified. Each control was asked to suggest a family member who could act as an informant.

Written informed consent was obtained from all controls and informants. Ethical approval for the study was obtained from the ethics committee, Institute of Psychiatry, London, UK, and the ethics review committee, Aga Khan University, Karachi, Pakistan.

Interview techniques

The psychological autopsy method was employed to obtain information. The main sources of information were the close family members of the suicide victims.

A semi-structured interview schedule was used, similar to one used in other psychological autopsy studies. Reference Foster, Gillespie and McClelland 12 , Reference Appleby, Cooper, Amos and Faragher 13 The mental state section consists of questions about symptoms, leading to an ICD–10 diagnosis. 14 The life-events section was derived from Paykel's Life Events Schedule and consisted of 44 life-events plus two ‘others’. Reference Paykel 15 There were at least three items that related directly to ‘loss of significant person’ (death of close friend/relative; death of spouse; miscarriage/abortion/ stillbirth) and four that related to other ‘loss events’ (move within the same city or area; move to another city or area; loss of objects of personal value; child leaving home). The personality section consisted of the Personality Assessment Schedule; Reference Tyrer and Alexander 16 this can be used to diagnose ICD–10 personality disorder categories and to identify less severe abnormalities of personality. All interviews (with informants for suicides and controls) were conducted by M.M.K.

Data analysis

Frequencies were computed to assess the distribution of demographic and educational characteristics, psychiatric symptoms, and diagnoses in suicide victims and controls. Only the principal diagnosis was used in analysis. Univariate analysis was conducted by computing unadjusted matched ORs and their 95% confidence intervals to compare suicides and controls with respect to different risk factors. The dependent variable was suicide or control status and independent variables were hypothesised risk factors. Multivariable conditional logistic regression was conducted to identify risk factors independently associated with suicide. Conditional logistic regression analysis was performed using SAS version 9.1 for Windows.

Of the 100 suicides, 83 were men and 17 were women, with a male:female ratio of 4.9:1.

Distribution of various socio-demographic variables among suicide victims and controls is given in online Table DS1. Those who died by suicide were less educated compared with controls; 21% of suicides and only 4% of controls had no formal education, 44% of suicides and 51% of controls had completed 6–12 grades, and 10% of suicides and 32% of controls had graduate-level education or above.

Of the suicide victims, 24% were married compared with only 11% of controls, whereas 51% of suicides and 73% of controls were single. The proportion of individuals who lived in joint/extended families was 62% among suicide victims and 66% among controls. Unemployment was more prevalent among suicides (39%) compared with controls (10%).

The method employed in suicide was as follows: hanging ( n =40), poisoning ( n =26), firearms ( n =15) and self-immolation ( n =10). The remaining 9 cases were: jumping from a height ( n =3), jumping in front of a train ( n =2), use of sharp instruments ( n =2), jumping into the sea ( n =1) and self-injection with narcotics ( n =1). There were no instances of medication overdose.

An ICD–10 principal diagnosis was found for 96 suicides and 6 controls ( Table 1 ) (McNemar test, P <0.001).

Table 1 ICD–10 principal diagnosis

Diagnosis	Cases ( =100)	Controls ( =100)
Moderate depressive episode (F32.1)	30	1
Severe depressive episode (F32.2)	43	0
Severe depressive episode with psychotic symptoms (F32.3)	6	2
Schizophrenia (F20)	6	2
Adjustment disorders (F43.2)	3	0
Acute stress reaction (F43.0)	6	0
Alcohol use (F10.0)	0	0
Substance abuse (F11.0)	1	0
Mental retardation (F79)	1	0
Personality disorder (F60)	1	1
No psychiatric diagnosis	4	94

Only three suicide victims and two controls were known psychiatric patients. Of the former, one was undergoing treatment by a psychiatrist and two by family physicians. All three were receiving irregular treatment. Only six suicide victims and two controls had made previous suicide attempts (McNemar test, P =0.289). For one control and one suicide, a first-degree relative had died by suicide; for one control (but no suicide victim) a first-degree relative had attempted suicide.

Family history of psychopathology was present for four suicides (three unipolar depression, one organic disorder) and four controls (two unipolar depression, one bipolar disorder and one anxiety disorder). One or more life-events were present in 66 suicides and 28 controls in the 6 months prior to suicide. Of these, ‘loss events’ were present in two suicides and one control.

Conditional logistic regression analysis

The univariate analysis of socio-demographic, psychiatric and life-event variables as potential risk factors is summarised in online Table DS1. Suicide victims were more likely to have received no formal education or only at primary level compared with controls (OR=4.2, 95% CI 2.0–8.7).

Suicide victims were about three times more likely to have ever been married compared with controls (OR=2.7, 95% CI 1.4–5.1). Moreover, there was a significant association between suicide and low social class (OR=3.4, 95% CI 1.5–8.0), unemployment/household work (OR=7.0, 95% CI 2.7–17.9), relatively isolated/disrupted social network (OR=9.5, 95% CI 4.1–22.0), and depression (OR=77.0, 95% CI 10.7–553.6). There was no association between suicide and religion, and the family structure (nuclear, joint/extended).

The univariate analysis of life-event factors indicated that suicides were more likely to have suffered a moderate/major impact of health problems (OR=2.5, 95% CI 0.8–8.0) and financial problems (OR=3.2, 95% CI 1.3–8.0) and of unemployment (OR=3.0, 95% CI 1.1–8.2) compared with controls.

The multivariable conditional logistic regression model included the independent effects of ICD–10 diagnosis of depression, educational attainment and marital status adjusted for employment status ( Table 2 ). There is overwhelming evidence of an association between depression and suicide after adjusting for education, employment and marital status (OR=208.2, 95% CI 11.0–3935.2). Suicide victims were more than three times more likely to have ever been married (OR=3.6, 95% CI 0.6–22.3) and about five times more likely to have received no formal education or only at primary level (OR=4.9, 95% CI 0.8–29.8) relative to controls. The interaction between employment and marital status was insignificant (likelihood ratio test, P >0.50). This could be noted as a limitation in terms of sample size leading to low power to detect the interaction that appears to be biologically meaningful.

Table 2 Final multivariable conditional logistic regression model

Variable	Adjusted OR (95% CI)
Educational attainment
No formal education/primary	4.9 (0.8–29.8)
Secondary and above	1.0
Marital status
Never married	1.0
Ever married	3.6 (0.6–22.3)
Depression
No	1.0
Yes	208.3 (11.0–3935.2)

a. Adjusted for employment status

Studies of suicide from Pakistan are few and mostly descriptive case series. Reference Saeed, Bashir, Khan, Iqbal, Raja and Rehman 17 – Reference Ahmed, Ahmed and Mubeen 19 This is the first study that has studied risk factors using a matched-pair case–control method for suicides in Pakistan.

Our results suggest that among those who died by suicide, 96 individuals had an ICD–10 psychiatric disorder. This finding is comparable to other psychological autopsy studies, which have also found mental disorders to be present in 80–100% of their samples, with depression as the most common primary diagnosis. Reference Foster, Gillespie and McClelland 12 , Reference Appleby, Cooper, Amos and Faragher 13 , Reference Cheng 20 – Reference Vijayakumar and Rajkumar 23

In our study, depression was the principal diagnosis in 79 people. This is contrary to what is generally reported as underlying suicides in Pakistan, i.e. interpersonal relationship problems, domestic disputes and financial problems. Reference Saeed, Bashir, Khan, Iqbal, Raja and Rehman 17 , Reference Ahmed and Zuberi 18 , Reference Shoaib, Nadeem and Khan 24 Mental illness is mentioned in only a small numbers of suicides. Many of these reports are based on police or forensic medicine data, which do not study psychological factors in suicides.

This approach has given rise to a ‘reductionist model’ that portrays the suicide victim in low- and middle-income countries like Pakistan as an impulsive individual who, for example, over-reacts to personal setbacks and in an emotional fit ingests an easily accessible but highly dangerous substance such as organophosphate pesticide. Reference Khan 3 The absence of good medical facilities for resuscitation leads to a high case:fatality ratio. Reference Eddleston, Sheriff and Hawton 25 This model holds the individual responsible for his/her actions, emphasising the immediate proximal factors (e.g. a row with significant other), while ignoring intermediate (e.g. depression) and distal factors (e.g. adverse social circumstances) which form the ground-work on which proximal factors act. Reference Shaikh and Rabbani 28

Among the suicides, there was only one person with a diagnosis of alcoholism, while three were reported to have consumed alcohol on the day of death. This finding is in contrast to other studies including Asian countries, where alcohol features prominently in suicides. Reference Cheng 20 , Reference Vijayakumar and Rajkumar 23 Alcohol is prohibited in Islam and legally banned in Pakistan. It has never been part of the culture and except in certain small sections of the society, is not used socially. In the absence of disinhibitory influence of alcohol, suicides in Pakistan appear to be less ‘contaminated’ or more ‘pure’. This observation needs further exploration.

The most common method of suicide was hanging, followed by poisoning. In Pakistan, poisoning is usually by organophosphate pesticides. These substances have a high ingestion:fatality ratio, compounded by absence of timely and proper medical management. In the West the case:fatality ratio from poisoning is estimated to be 1–2%, but in low- and middle-income countries this can be as high as 12–15%. Reference Eddleston, Sheriff and Hawton 25

Firearms were used by 15% of suicides. Compared with earlier studies Reference Ahmed and Zuberi 18 there has been a significant increase in the use of firearms, which reflects the fast-growing problem of firearms availability in Pakistan over the past few years.

Among suicides, the number of single people (51%) was almost equal to those ever-married, whereas in controls 73% were single and 27% ever-married. This difference remained striking in the multiple conditional logistic regression model. Unlike in the West, where marriage is protective, in Pakistan, marriage appears to be a significant source of stress (especially for women), leading to high psychological morbidity and suicidal behaviour. Reference Qadir, de Silva, Prince and Khan 27

When questioned about their observations of any changes in the victims prior to the suicide, 90% of the relatives responded that in the days and weeks prior to the suicide the victim did not appear ‘their usual self’. Of these, 34% felt the individual was under some sort of ‘mental pressure’, while 22% considered it to be serious enough to ‘consult a doctor’. Of these 22, 5 individuals refused to see a doctor, while in 17 cases family members did not know where to seek help or have the resources to pay the doctors' fees.

Limitations

There were several limitations in our study: the ascertainment of suicide by police was one. It is possible that some potential risk factors may also be factors associated with the police's misclassification of some suicides; for example, underestimating suicide among people of higher social class, practising Muslims, the employed and use of certain methods such as burning. The small number of personality disorders may be related to informants' reluctance to talk negatively about their deceased relative. The low number of cases with previous history of self-harm, and family history of suicide and self-harm were similar to those from China, Reference Phillips, Yang, Zhang, Wang, Ji and Zhou 22 India Reference Vijayakumar and Rajkumar 23 and Sri Lanka Reference Eddleston, Sheriff and Hawton 25 that also show high fatality in first-time attempters, although not in others such as Taiwan. Reference Cheng, Chen, Chen and Jenkins 21 Another limitation was the low number of psychiatric disorders in controls, whereas population-based prevalence studies for depression give high figures for Pakistan (15–33% for men and 40–66% for women). Reference Mirza and Jenkins 11 Very likely, there was underestimation of psychiatric morbidity in controls. Despite every effort to keep interviews as objective as possible, the fact there was no masking of suicide or control status meant the quality of interviews may have been lower in controls.

Implications for suicide prevention in Pakistan

Our study has important implications for suicide prevention in Pakistan. The majority of suicide victims died at their first attempt, showing a high case fatality rate and lethality of method. Also, although the majority of suicide victims had a mental disorder, only a small minority were receiving psychiatric treatment at the time of death. No victim made contact with health facilities during the month before suicide, and only nine people ever received treatment for psychiatric disorders in the past.

These findings imply that in Pakistan, suicide prevention interventions would have to be pre-emptive rather than reactive, i.e. before the first attempt, either at the stage of suicidal ideation or earlier. Low-cost community-based mental health programmes that would detect and treat mental disorders at an early stage would be an effective way to address this.

In low- and middle-income countries like Pakistan, there is a need for effective health systems with a primary care/public health approach, of which mental health is an integral part. Unfortunately, the primary healthcare system in Pakistan remains largely ineffective, inefficient, poorly organised and underfunded. Reference Shaikh and Rabbani 28 For mental health to be integrated, the primary health system itself would need to be improved substantially.

Training primary care health professionals in detection and management of depression has been shown to lower suicide rates. Reference Rutz, von Knorring and Wålinder 29 Strategies that restrict access to lethal means of suicide, for example pesticides, have also been found to be effective. Reference Bowles 30 These have important implications for suicide prevention in Pakistan.

Acknowledgements

We thank Dr Jayne Cooper, University of Manchester, UK, for providing the psychological autopsy instrument used in this study. We also gratefully acknowledge the help of Mr Tariq Jamil and Mr Reza Shah of Capital City Police, Karachi, Pakistan, whose help was crucial in conducting this study. This work was supported by a grant from the University Research Council (URC), Aga Khan University, Karachi, Pakistan (URC grant ID no: 021014PSY).

Presented in part at the XXIII World Congress of International Association of Suicide Prevention, Durban, South Africa, 12–16 September 2005.

Declaration of interest

None. Funding detailed in Acknowledgements.

Khan et al. supplementary material

Supplementary Table S1

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Volume 193, Issue 5
Murad Moosa Khan (a1) , Sadia Mahmud (a2) , Mehtab S. Karim (a2) , Mohammad Zaman (a1) and Martin Prince (a3)
DOI: https://doi.org/10.1192/bjp.bp.107.042069

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v.11(8); 2017 Aug

Prevalence and Severity of Depression in a Pakistani Population with at least One Major Chronic Disease

Ansab godil.

1 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Muhammad Saad Ali Mallick

2 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Arsalan Majeed Adam

3 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

4 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Akash Khetpal

5 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Razna Afzal

6 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Maliha Salim

7 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Naureen Shahid

8 Medical Student, Department of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan.

Introduction

Diabetes, anaemia, hypertension and asthma are major contributors to morbidity in our society. Depression is the commonest psychological malady diagnosed in hospital settings. There tends to be some overlap between certain chronic systemic illnesses and depressive disorders, this point towards the need to determine relationships between them, if any.

To determine the prevalence and compare the severity of depression among individuals diagnosed with four of the most common chronic diseases in our community.

Materials and Methods

This cross-sectional study was carried out among patients with chronic diseases visiting a tertiary care hospital in Karachi, Pakistan from August 2015 to August 2016. The Beck Depression Inventory-II*, a 21-item self-report instrument was used to assess the severity of depression. Categorical variables were compared using Chi-square test while intergroup comparisons were performed using one way ANOVA test. Logistic regression was employed to estimate the odds of Category B depression (moderate and severe levels of depression) in chronic diseases.

The prevalence of anaemia, hypertension, diabetes and asthma was 90%, 47%, 26% and 23% respectively. Predictors of Category B depression were anaemia (OR=4.21, 95% CI: 1.30-13.56) and diabetes (OR=2.03, 95% CI: 1.09-3.77). Asthma predicted Category B depression in males (OR=1.26, 95% CI: 0.29-5.42) but not in females (OR=0.77, 95% CI: 0.39-1.52). Individuals with hypertension were less likely to report Category B depression than non-hypertensive (OR=0.72, 95% CI 0.43-1.21). Female gender had a greater influence to develop Category B depression than males (OR= 2.96, 95% CI: 1.93-4.55).

Our study points towards a strong correlation between depression and chronic diseases especially anaemia and diabetes. This cautions medical practitioners against treatment of depressive disorders and chronic diseases as separate, independent entities.

Depression is a mental health disorder wherein low mood and low energy can affect a person’s thoughts, feelings, behaviour and sense of well-being [ 1 ]. It is characterized by disturbed sleeping pattern, change in appetite, fatigue, irritability, reduced ability to concentrate, difficulty in decision making and even suicidal thoughts. Depression is a common psychological state affecting over 350 million people from all age groups [ 2 ]. Unipolar depressive disorder is expected to be the most significant cause of disease burden by the year 2030 [ 3 ]. Marked as one of the most common unidentified mental health problems in Pakistan, masked by long-term illnesses and psychological disturbances, depression plays a key role in worsening the prognosis of chronic diseases. The risk of developing depression in the general population is 10%-25% in females and 5%-12% in males; whereas, in patients with chronic conditions the risk increases up to 25%-33% [ 4 ]. Chronic conditions such as diabetes mellitus, asthma, hypertension and anaemia are the most common comorbidities in a hospital setting.

Diabetes is a group of metabolic syndromes with uncontrolled high levels of blood glucose. Type I diabetes, also known as Insulin-Dependent Diabetes Mellitus (IDDM), is a genetic disorder resulting in inability of pancreatic beta cells to produce insulin. Type II diabetes, also known as Non-Insulin-Dependent Diabetes Mellitus (NIDDM), is caused by ‘insulin resistance’ i.e., target cells stop responding to insulin. It is strongly associated with a sedentary life style and obesity. Depression is an important comorbid of both Type I and Type II diabetes, possibly because diabetes requires significant lifestyle changes to cope with the disease. Changes with regard to controlling blood sugar through dietary restrictions lead the way for depressive symptoms as early as the person is diagnosed with the disease. It has been demonstrated that the prevalence of depression is higher in diabetics than in non-diabetics [ 5 - 7 ] and approximately 43 million patients with diabetes suffer with depressive symptoms [ 8 ].

Asthma, another common chronic condition affected by both genetic and environmental factors, is an inflammatory disease of the upper respiratory tract. It is characterized by reversible episodes of airway obstruction, bronchospasm, shortness of breath, wheezing and coughing. Symptoms of severe asthma, such as dyspnoea (shortness of breath) leading to wakening of a patient from sleep, has a strong correlation with depression [ 9 ].

One of the most commonly prevailing long term illnesses includes hypertension. It is defined as arterial blood pressure of more than 140/90 mmHg. It is a chronic disease that requires drastic lifestyle and dietary modifications in order to maintain a normal blood pressure. A study highlighted a three times higher frequency of depressive symptoms in hypertensive patients [ 10 ], hence there is a need for reassurance and psychological feedback in hypertensive patients.

Anaemia, as defined by the World Health Organization (WHO), is the blood plasma Haemoglobin (Hb) concentration of less than 12 g/dl in women and 13 g/dl in men [ 11 ]. There are several theories in medical literature linking anaemia and depression. Anaemia is strongly associated with decreased muscle strength and fatigue (due to reduced oxygenation), adversely affecting a patient’s quality of life which can facilitate the development of depressive symptoms in an anemic individual [ 12 , 13 ].

The purpose of this study is to highlight the prevalence of depression among individuals diagnosed with four of the most common chronic diseases i.e., diabetes, asthma, hypertension and anaemia. It also seeks to compare the severity of depressive symptoms amongst each of the chronic diseases.

This cross-sectional study was conducted at a Civil Hospital of Karachi, Pakistan. The study protocol was approved by local Ethical Committee and prior to obtaining consent, all participants were explained about the purpose of the study and the relevant procedures involved. The study duration was one year from August 2015 to August 2016. A total of 515 patients who had been admitted to the medical wards around the year were evaluated. The participant’s cooperation rate was 95% which yielded a final sample size of 489. Participants were selected via convenience sampling.

Patients with psychiatric disorders, any type of cognitive impairment such as dementia and mental retardation, patients on anti-depressants, females in post-partum period and patients who had undergone any traumatic event within the last six months were excluded from the study. Patients above 18 years of age; confirmed diagnosis of at least any one of the following chronic illnesses: diabetes, asthma, hypertension and anaemia; and patients who could speak and understand Urdu (the questionnaire was translated into Urdu for easier and unambiguous communication with the local population) were included in the study.

Previously diagnosed patients with diabetes and asthma were put into respective categories; patients who had a history of consuming anti-hypertensive medications were classified as hypertensives; patients with consistently low levels of Hb in their previous medical records were classified as anaemics. A pilot study was conducted on 40 patients (who were not included in the total sample) to test and rectify any shortcomings in the study questionnaire. Interviewer’s bias was reduced by selecting individuals with the same academic background, training them and keeping them unaware of the study’s results.

The final questionnaire was divided into three sections ‘medical history and demographic details’, ‘laboratory values’ and ‘Beck Depression Inventory (BDI) scale’. The Beck Depression Inventory Second Edition (BDI-II), a 21-item self-report instrument was used to assess the severity of depression via scores assigned to each question. Total score of 0-13 is ‘minimal’, 14-19 is ‘mild’, 20-28 is ‘moderate’ and 29-63 is ‘severe’ [ 14 ].

Statistical Analysis

The data was entered manually into the SPSS Statistics, version 17.0 (IBM SPSS Inc., Chicago, IL). No imputation method was used to replace missing values and only completely filled questionnaires were included in the study. The normality was assessed using Shapiro-Wilk test. All the categorical variables were expressed as frequencies (percentages) and compared by the Pearson’s Chi-square test. Age, a continuous variable was expressed as mean±standard deviation and intergroup comparisons were performed using one way ANOVA test. Random Blood Sugar (RBS), Hb, duration of diabetes, hypertension and asthma were divided into two categories each, according to their median values.

The four classes of depression according to BDI scale (mild, minimal, moderate, severe) were divided into two categories A and B i.e., less severe and more severe depression respectively. Category A included mild and minimal levels of depression whereas Category B included moderate and severe levels of depression.

Logistic regression models were applied in order to determine the association of Category B depression (dependent variable) with each chronic disease (independent variable). Unadjusted and adjusted models 1 and 2, Odds Ratio (OR) and 95% Confidence Interval (CI) were calculated. In Model 1, age, marital status, RBS level and Hb level were adjusted. In Model 2, the rest of the chronic diseases along with the variables mentioned in Model 1 were adjusted. In total samples, both models were further adjusted for gender.

Similarly, logistic regression was applied to determine the association of socio-demographic factors like age, gender, marital status and laboratory indices like RBS and Hb (independent variable) with severity of depression among individuals having atleast one chronic illness (dependent variable). A two-tailed p-value<0.05 was considered statistically significant.

Mean±Standard Deviation (SD) age of the study sample was 42.18 (±15.74). Majority of the individuals were females (n=289, 59.1%), belonged to 40-60 age group (n=249, 50.9%) and were married (n=390, 79.8%). The mean±SD BDI score of the total sample was 17.78±8.91 (range 0-63). Most of the participants (n=185, 37.8%) were categorised under the minimal classification of BDI scale. Severely depressed individuals were almost one-third of the minimal depressed group of which mostly were females (n=55, 89%) and many were from youngest group (n=20, 32%). Amongst the participants with RBS >138 mg/dl, most of them were minimally depressed (n=132, 55.2%), while amongst those participants with RBS<138, most of them were mildly depressed (n=84, 33.6%). A similar pattern was observed in participants with Hb>10 and Hb<10, respectively [ Table/Fig-1 ]. Of the total, 32.9% (n=161) patients had one, 49.9% (n=244) patients had two, 14.1% (n=69) patients had three and only 3.1% (n=15) had all the four chronic diseases [ Table/Fig-2 ].

[Table/Fig-1]:

Frequency distribution of participants by socio-demographic features and selected variables.

Characteristic	Depression Scale				±p-value
Characteristic	Minimal (0-13) n=185	Mild (14-19) n=149	Moderate (20-28) n=93	Severe (29-63) n=62	±p-value
Age (years), mean±SD	43.22±13.3	45.56±18.14	39.44±12.22	35.03±18.19	±±0.001
					<0.001
<20	14 (8)	2 (1)	0 (0)	20 (32)
20-40	37 (20)	59 (40)	49 (53)	14 (23)
40-60	126 (68)	64 (43)	40 (43)	19 (31)
>60	8 (4)	24 (16)	4 (4)	9 (15)
					<0.001
Female	91 (49)	83 (56)	60 (65)	55 (89)
Male	94 (51)	66 (44)	33 (35)	7 (11)
					<0.001
Single	25 (14)	9 (6)	11 (12)	27 (44)
Married	153 (83)	138 (93)	77 (83)	22 (35)
Other	7 (4)	2 (1)	5 (5)	13 (21)
					<0.001
<138	53 (29)	84 (56)	67 (72)	46 (74)
>138	132 (71)	65 (44)	26 (28)	16 (26)
					<0.001
<10	100 (54)	103 (69)	54 (58)	17 (27)
>10	85 (46)	46 (31)	39 (42)	45 (73)

Data presented as frequency (percentages) and means±SD

±p value<0.05 was considered statistically significant

±±One-way ANOVA was used to compare continuous variable that was normally distributed

Hb: Haemoglobin; RBS: Random Blood Sugar; SD: Standard Deviation.

An external file that holds a picture, illustration, etc.
Object name is jcdr-11-OC05-g001.jpg

Bar chart showing frequency of patients having one or more chronic diseases.

The prevalence of anaemia, hypertension, diabetes and asthma was 90%, 47%, 26% and 23% respectively. Anaemia was the most prevalent chronic illness with females predominantly affected (n=274, 62.3%). Amongst anaemics, most of them had minimal depression (n=156, 35.5%) while most of the severely depressed ones were females (n=53, 85%). Nearly half of the hypertensives (n=109, 47.6%) were minimally depressed in contrast to only a small portion of them (n=25, 10.9%) afflicted with severe depression. Among diabetics, there were an equal number suffering from minimal and mild depression (n=48, 38.1%) and an equal number suffering from moderate and severe depression (n=15, 11.9%). Asthma was the least prevalent disease in our sample. Asthmatics had mostly minimal (n=44, 39.3%) and moderate levels of depression (n=32, 28.6%). Most of the patients who had one chronic disease were mildly depressed (n=53, 32.9%) while a majority of the patients who had two (n=106, 43.4%) or three (n=33, 47.8%) chronic diseases were categorized under the minimal classification of BDI scale. Those patients who had all four chronic diseases were mostly severely depressed (n=7, 46.7%) [ Table/Fig-3 ].

[Table/Fig-3]:

Frequency and duration of chronic disease according to BDI scale.

Characteristic	Ddepression Scale				±p-value
Characteristic	Minimal (0-13) n=185	Mild (14-19) n=149	Moderate (20-28) n=93	Severe (29-63) n=62	±p-value
					0.049
Yes	48 (26)	48 (32)	15 (16)	15 (24)
No	137 (74)	101 (68)	78 (84)	47 (76)
					0.065
<7	24 (13)	31 (21)	8 (9)	6 (10)
>7	24 (13)	17 (11)	7 (8)	9 (15)
					0.568
Yes	44 (24)	32 (21)	25 (27)	11 (18)
No	141 (8)	117 (79)	68 (73)	51 (82)
					0.158
<25	24 (13)	13 (9)	16 (17)	2 (3)	0.158
					<0.001
Yes	109 (59)	55 (37)	40 (43)	25 (40)
No	76 (41)	94 (63)	53 (57)	37 (60)
					<0.001
<5	47 (25)	38 (21)	24 (26)	7 (11)
>5	62 (34)	17 (11)	16 (17)	18 (29)
					<0.001
Yes	65 (35)	64 (43)	30 (32)	7 (11)
No	29 (16)	2 (1)	3 (3)	0 (0)
					<0.001
Yes	91 (49)	70 (47)	60 (65)	53 (85)
No	0 (0)	13 (9)	0 (0)	2 (3)
					<0.001
1	46 (24.9)	53 (35.6)	28 (30.1)	34 (54.8)
2	106 (57.3)	72 (48.3)	52 (55.9)	14 (22.6)
3	33 (17.8)	18 (12.1)	11 (11.8)	7 (11.3)
4	0 (0)	6 (4)	2 (2.2)	7 (11.3)

Data presented as frequency (percentages)

± p-value<0.05 was considered statistically significant

Pearson’s Chi-square (χ 2 ) test was used to compare categorical variables; HTN: Hypertension

[ Table/Fig-4 ] shows unadjusted and adjusted OR and 95% CI for Category B depression in the four major chronic diseases (i.e., presence vs. absence of each disease) in each gender for the total sample (more detail on adjusted model 1 and 2 is given in statistical analysis). Predictors of Category B depression in the fully adjusted Model 2 were anaemia (OR=4.21) and diabetes (OR=2.03). It should be noted that asthma predicted Category B depression in males (OR=1.26) but not in females (OR=0.77). Moreover, anaemic females were 9.3 times more likely to report Category B depression than non-anaemic females. Similarly, anaemic males were 2.4 times more likely to report Category B depression than non-anaemic males.

[Table/Fig-4]:

Category B depression in individuals with vs. without chronic disease.

Chronic disease	Male OR(95% CI )	Female OR(95% CI )	Total OR(95% CI )

Unadjusted	0.796 (0.376-1.685)	0.622 (0.336-1.149)	0.595 (0.374-0.946)
Model 1	1.531 (0.471-4.977)	1.567 (0.712-3.450)	1.638 (0.900-2.979)
Model 2	1.475 (0.310-7.021)	1.580 (0.714-3.495)	2.027 (1.089-3.772)

Unadjusted	1.051 (0.525-2.105)	0.622 (0.386-1.003)	0.749 (0.510-1.100)
Model 1	0.861 (0.324-2.285)	0.639 (0.329-1.238)	0.742 (0.448-1.229)
Model 2	0.913 (0.304-2.736)	0.570 (0.283-1.147)	0.719 (0.426-1.212)

Unadjusted	1.713 (0.768-3.822)	0.751 (0.431-1.308)	1.027 (0.653-1.614)
Model 1	1.787 (0.572-5.587)	0.961 (0.500-1.850)	1.086 (0.642-1.836)
Model 2	1.257 (0.292-5.419)	0.768 (0.389-1.518)	0.888 (0.516-1.529)

Unadjusted	2.964 (0.858-10.244)	4.562 (1.010-20.610)	4.552 (1.768-11.720)
Model 1	2.683 (0.568-12.667)	7.408 (1.149-47.769)	3.432 (1.118-10.534)
Model 2	2.434 (0.470-12.604)	9.343 (1.435-60.817)	4.205 (1.304-13.557)

Both models in ‘Total’ sample were further adjusted for gender.

Individuals with hypertension were less likely to report Category B depression than non-hypertensives (OR=0.72).

Overall female gender had a greater influence to develop Category B depression (OR=2.96). Participants with RBS <138 were about 4.0 times more likely to develop Category B depression than those with RBS>138. While participants with Hb >10 were approximately 1.9 times more likely to develop Category B depression than those with Hb <10. Males with RBS <138 were 7.1 times more likely to report Category B depression than those with RBS >138. In contrast, females with RBS <138 were 3.1 times more likely to report Category B depression than those with RBS >138. Males with Hb >10 were 3.2 times more likely to report Category B depression than those with Hb <10. In contrast, females with Hb >10 were 4.2 times more likely to report Category B depression than those with Hb <10 [ Table/Fig-5 ].

[Table/Fig-5]:

Socio-demographic factors and laboratory indices predicting Category B depression in participants with at least one chronic disease.

Variables	Male OR(95% CI )	Female OR(95% CI )	Total OR(95% CI )
Age	0.923 (0.890-0.956)	1.004 (0.987-1.021)	0.973 (0.960-0.986)

Male	-	-	1.0
Female	-	-	2.960 (1.927-4.546)

Single	NC	1.0	1.0
Married		0.378 (0.212-0.672)	0.301 (0.180-0.506)
Others		1.611 (0.631-4.115)	1.789 (0.710-4.510)

<138	7.143 (2.995-17.268)	3.065 (1.844-5.096)	4.039 (2.641-6.179)
>138	1.0	1.0	1.0

<10	1.0	1.0	1.0
>10	3.184 (1.255-8.073)	4.201 (2.406-7.336)	1.853 (1.256-2.733)

RBS: random blood sugar; Hb: Haemoglobin.

This report represents the first epidemiologic study on the frequency and severity of depression in four of the most common chronic diseases in Pakistan; anaemia, hypertension, diabetes and asthma. Similar to previous studies, we found a significant association between depression and the aforementioned chronic conditions in our community. We also found a higher depression risk in patients with anaemia and hypertension as compared to asthma and diabetes. Association of depression with chronic diseases is well established in previous literature [ 15 , 16 ]. A cross-sectional study conducted by Patten SB et al., found an increased risk of major depression in patients with chronic medical disorders compared to those without such disorders (4% vs. 2.8%) [ 17 ]. Burden of medical bills, fear of losing one’s job and reduction in earning power may be a potentiating factor for developing depression in these patients. This situation is alarming as it could have a negative impact on the patient’s well-being. In spite of the elevated morbidity, disability, mortality and reduced quality of life, comorbid depression continues to be under-recognized and undertreated [ 18 - 21 ], possibly due to the stigma attached to it leading to poor patient compliance. An understanding of the course of depression and its masked presentation is crucial to the medical management of patients with chronic illness. Comorbid depression is associated with increased symptom burden; functional impairment; greater costs due to overutilization of medical services; poor adherence to lifestyle alterations such as diet control, regular exercise, abstinence from smoking and timely medications; as well as direct pathophysiological effects on inflammatory mediators, metabolic parameters hypothalamic-pituitary pathway and the autonomic nervous system [ 22 ].

Our results also illustrated that most of the patients were categorized under the minimal classification of BDI scale. As opposed to findings from western literature where the severity of depression is slightly higher, there are several protective elements that may inhibit the development of depression in our community, explaining the low incidence of comorbid depression in our population. These factors include the eastern cultural values and the extended family systems. Several studies from the West and Asia have presented that social support reduces the development of depressive symptoms in people with chronic disorders [ 23 , 24 ]. Familial relations and interactions within a closely knitted community are of particular importance in Pakistani population, and family support is vital especially in times of illness and during treatment. A chronically ill individual should be advised to establish good familial relations. Along with the patient, the attendants should be counselled in their role in patient satisfaction and betterment. As was expected, patients suffering from all four chronic diseases manifested with severe depression in our study probably due to a poor quality of life and increased medical expenses from managing so many ailments.

Our data also shows that women are significantly more likely to be depressed as compared to men. A previous study reported prevalence of depressive symptoms was more in women than in men (19.7% vs. 13.9%) [ 17 ]. Prior studies have implicated a role for female hormones, such as estrogen, however the relationship of depression and estrogen is very diverging with studies establishing both positive and negative association [ 25 , 26 ]. Furthermore, education is likely to enhance female independence: women develop greater confidence and capabilities to make decisions regarding their own health. Educated individuals are more likely to seek medical care and consequently become diagnosed with depression and chronic disease [ 27 ]. Women from our setup have little to no education, leaving them more dependent on others during their illness. Additionally, previous studies have noted women’s higher vulnerability to the adverse mental health effects of a lower socioeconomic status as compared to men [ 27 ]. Public health policy can benefit from understanding gender differences to better address the mental health needs of the community. Another noteworthy finding is that most severely depressed patients belonged to a younger age group. We generally do not anticipate chronic diseases at a younger age; however, the ones that do develop such diseases earlier in life report greater depressive symptoms than those who develop them later [ 28 ].

Moreover, there is a greater sense of hopelessness as compared to older individuals as they see a lifetime ahead with a debilitating condition which may compromise their quality of life.

Anaemia was found to be the highest prevailing chronic illness in our set-up with the highest frequency of comorbid depression. According to WHO and the National Health Survey of Pakistan (NHSP), among Pakistani non-pregnant women aged 15-49 years, 51% had blood haemoglobin concentration of less than 12 g/dl and overall mean blood haemoglobin concentration was 11.7 g/ dl whereas haemoglobin in young men varied from 12% to 28% depending on socioeconomic status [ 29 , 30 ]. Several theories have been postulated for the relationship of depression with anaemia. Firstly, reduced muscle strength and weakness are commonly associated with anaemia and may have a negative effect on the patient’s quality of life, therefore promoting the development of depressive symptoms [ 31 ]. Secondly, malnutrition, a common cause of anaemia, can lead to development of comorbid depression. Majority of the population coming to a tertiary care hospital setting belong to a low socioeconomic group; hence, have a poor nutritional status. Patients with comorbid depression visit a healthcare facility more frequently as compared to medically ill patients without depression, which means that the physician has more opportunities to screen and monitor the mental health status of these individuals. Himelhoch S et al., illustrated that emergency room visits are two to three times more common among patients with diabetes and hypertension who have depression as opposed to chronically ill patients without depression [ 32 ]. One possible explanation is that depressed patients have an enhanced perception and a greater sensitivity to physical symptoms [ 33 ]. Furthermore, the presence of a chronic condition may reduce the probability of health care providers to recognize or treat depression as they may overlook non-specific symptoms such as fatigue, poor concentration and a general lack of interest. Depression can hinder the patient’s involvement in the treatment plan; therefore, it becomes clinically significant to anticipate when a patient with a chronic condition may develop comorbid depression. General physicians are the backbone of health care system in Pakistan with majority of the population first visiting a general physician. These doctors mostly practice solo and do not have the medical expertise for identifying a mental health crisis. Therefore, they must be trained to identify the presence of depression when patients present with a chronic physical condition. In addition, promoting public awareness can help in countering the stigma surrounding mental illness and can alert health personnel as well as the general public that depression is as damaging to health as a physical condition.

There are several limitations in our study which need to be considered. Firstly, we considered patients only from a single tertiary hospital. Although Civil Hospital, one of the largest hospitals in Pakistan, is a medical centre where people come from all parts of the city, we believe that including other hospitals would have increased the strength of our results and helped us to generalize the findings. Secondly, there is a possibility of getting amplified scores on depression scales due to the somatic symptoms of the disease. For instance, asthma can cause insomnia and asthma medications such as β-agonists can cause anxiety; both of these symptoms are part of the depression scale we used and can alter the final tabulated score. The low rate of diabetes in our study is understated, since it is uncommon for people in our setup to undergo routine exams and laboratory tests for detection. Finally, findings from this study may not be applicable to other countries in the region, or even to different regions of Pakistan.

In future, larger studies with multiple hospitals nationwide should be conducted regarding depression and chronic medical illnesses.

A worthwhile field for research includes investigating the effect of psychological and behavioural interventions in the physically ill. We also suggest that epidemiologic studies should control for other comorbid chronic conditions in their analysis of such an association.

Financial or Other Competing Interests

This paper is in the following e-collection/theme issue:

Published on 24.6.2024 in Vol 26 (2024)

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study

Authors of this article:

Original Paper

Rubèn González-Colom 1 , PhD ;
Kangkana Mitra 1 , MSc ;
Emili Vela 2, 3 , MSc ;
Andras Gezsi 4 , PhD ;
Teemu Paajanen 5 , PhD ;
Zsófia Gál 6, 7 , MSc ;
Gabor Hullam 4, 7 , PhD ;
Hannu Mäkinen 5 , PhD ;
Tamas Nagy 4, 6, 7 , MSc ;
Mikko Kuokkanen 5, 8, 9 , PhD ;
Jordi Piera-Jiménez 2, 3, 10 , PhD ;
Josep Roca 1, 11, 12 , MD, PhD ;
Peter Antal 4 * , PhD ;
Gabriella Juhasz 6, 7 * , PhD ;
Isaac Cano 1, 12 * , PhD

1 Fundació de Recerca Clínic Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain

2 Catalan Health Service, Barcelona, Spain

3 Digitalization for the Sustainability of the Healthcare - Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain

4 Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary

5 Department of Public Health and Welfare, Finnish Health and Welfare Institute, Helsinki, Finland

6 Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary

7 NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary

8 Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine at University of Texas Rio Grande Valley, Brownsville, TX, United States

9 Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland

10 Faculty of Informatics, Telecommunications and Multimedia, Universitat Oberta de Catalunya, Barcelona, Spain

11 Hospital Clínic de Barcelona, Barcelona, Spain

12 Faculty of Medicine, Universitat de Barcelona, Barcelona, Spain

*these authors contributed equally

Corresponding Author:

Rubèn González-Colom, PhD

Fundació de Recerca Clínic Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer

C/Rosselló 149-153

Barcelona, 08036

Phone: 34 932275707

Email: [email protected]

Background: Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions.

Objective: This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions.

Methods: In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland.

Results: The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants ( P <.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases.

Conclusions: The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Introduction

The co-occurrence of multiple chronic diseases, known as multimorbidity [ 1 ], affects 1 in 3 adults. Its prevalence rises with age, affecting 60% of individuals aged between 65 and 74 years and escalating to 80% among those aged ≥85 years [ 2 ]. Due to its association with poor prognosis, functional impairment, and reduced quality of life, multimorbidity is considered a global health care challenge [ 3 , 4 ] tied to complex clinical situations, leading to increased encounters with health care professionals, hospitalizations, and pharmacological prescriptions, resulting in a substantial rise in health care costs [ 5 ]. The emergence of multimorbidity is not arbitrary and frequently aligns with shared risk factors and underlying pathophysiological mechanisms [ 6 - 8 ] that result from complex interactions between genetic and environmental factors throughout the life span [ 9 ]. Perceiving diseases not in isolation but as integral components of a more extensive, interconnected system within the human body has led to the emergence of network medicine [ 10 , 11 ]. Network medicine analyzes disease co-occurrence patterns, aiming to understand the complex connections between diseases to uncover biomarkers, therapeutic targets, and potential interventions [ 12 , 13 ]. The studies investigating the temporal patterns of disease concurrence, or disease trajectories [ 14 , 15 ], rely on a pragmatic approach to this concept to yield a better understanding of the time-dependent relationships among diseases and establish a promising landscape to identify disease-disease causal relationships.

According to this paradigm, a disease-centered approach might lead to suboptimal treatment of patients with multiple chronic conditions, triggering the need to implement new tools to enhance the effectiveness of health services [ 16 ]. In this regard, multimorbidity-adjusted health risk assessment (HRA) tools [ 17 - 21 ], such as the morbidity groupers, are crucial for assessing the comprehensive health needs of patients with multimorbidity [ 22 ]. HRA uses algorithms and patient data to categorize individuals by risk, aiding health care professionals in customizing interventions, optimizing resource allocation, and enhancing patient outcomes through preventive care. HRA tools facilitate efficient case-finding and screening processes [ 23 ]. Case finding targets the individuals at high risk, which is crucial for specialized health care programs, whereas patient screening detects latent illnesses early, enabling cost-effective interventions to prevent disease progression and reduce health care demands.

However, despite their widespread use, prevailing population-based HRA tools such as the Adjusted Clinical Groups [ 24 ], Clinical Risk Groups [ 25 ], or Adjusted Morbidity Groups (AMG) [ 4 , 21 ] still do not incorporate information on disease trajectories in their calculations. The AMG system is currently used in Catalonia (Spain; 7 million inhabitants) for health policy and clinical purposes. Adding disease-disease association information to the AMG (or other morbidity groupers) may open new avenues for implementing epidemiological impact analyses concerning clusters of interrelated diseases. In addition, it may facilitate the construction of risk groups that accurately represent probabilities of developing new associated disease conditions [ 26 ] susceptible to early prevention.

While acknowledging current limitations, this study sought to explore the feasibility of incorporating procedures relevant to the study of disease trajectories [ 14 , 15 ] and novel techniques for analyzing dependency relationships between concomitant diseases [ 27 , 28 ] to improve the capabilities of the current morbidity groupers. This approach might better adjust the estimations of the burden of morbidity to clusters of diseases and improve the ability to anticipate the progression of multimorbidity.

We used major depressive disorder (MDD; F32-F33 in the International Classification of Diseases, 10th Revision, Clinical Modification [ ICD-10-CM ] [ 29 ]) as a use case due to its clinical relevance in multimorbidity management. However, this study pursued to showcase a methodology applicable beyond MDD, allowing for the assessment of the impact of multimorbidity across different clusters of diseases.

This paper describes the process of development and assessment of the Multimorbidity-Adjusted Disability Score (MADS) through an observational retrospective multicentric cohort study, showcasing a pioneering approach that integrates advanced techniques for analyzing disease associations, insights from the analysis of disease trajectories, and a comprehensive scoring method aimed at evaluating the disease burden. The MADS was designed to stratify patients with different health needs according to (1) the disease burden caused by MDD and its comorbidities on individuals and health systems and (2) the risk of morbidity progression and the onset of MDD comorbid conditions.

On the basis of the temporal disease maps among MDD and highly prevalent disease conditions [ 30 ] generated using Bayesian direct multimorbidity maps (BDMMs) [ 27 , 28 ], a promising method for filtering indirect disease associations in the context of the European Research Area on Personalized Medicine project “Temporal disease map based stratification of depression-related multimorbidities: towards quantitative investigations of patient trajectories and predictions of multi-target drug candidates” (TRAJECTOME) [ 31 ], we combined the probabilities of relevance (PRs) among MDD and its comorbid conditions with the disability weights (DWs) [ 32 ], documented in the 2019 revision of the Global Burden of Disease (GBD) study, to compute the MADS. We used the MADS to generate a risk pyramid and stratify the study population into 5 risk groups using different percentiles of MADS distribution. Finally, we analyzed the correspondence between the MADS risk groups and health outcomes through a cross-sectional analysis of mortality and use of health care resources and a longitudinal analysis of disease prevalence and incidence of new disease onsets. The clinical relevance of the identified risk groups was assessed through a multicentric assessment of the findings. To this end, MADS performance was analyzed using data from 3 independent European cohorts from the United Kingdom, Finland, and Spain including >1 million individuals.

The development and evaluation of the MADS involved the following steps ( Figure 1 ).

Step 1 involved computing age-dependent disease-disease PRs using the BDMM method in 4 age intervals (0-20 years, 0-40 years, 0-60 years, and 0-70 years). This analysis resulted in an inhomogeneous dynamic Bayesian network that determined the PR for MDD against the most prevalent co-occurring diseases in the 3 European cohorts considered in TRAJECTOME, namely, the Catalan Health Surveillance System (CHSS) [ 33 ], the UK Biobank (UKB) [ 34 ], and the Finnish Institute for Health and Welfare (THL) [ 35 ] cohorts. The THL cohort amalgamates information from the FINRISK [ 36 ] 1992, 1997, 2002, 2007, and 2012; the FinHealth [ 37 ] 2017; and the Health [ 38 ] 2000 and 2011 studies.

In step 2, combining the PR of every disease condition assessed in the study with its corresponding DW extracted from the GBD 2019 study, we estimated the morbidity burden caused by MDD and its comorbid conditions. The MADS was computed following a multiplicative combination of the PR and DW of all the disease conditions present in an individual.

Step 3 involved using the MADS to stratify patients into different risk levels corresponding to different percentiles of the population-based risk pyramid of each patient cohort: (1) very low risk (percentile ≤50), (2) low risk (percentile 50 to percentile 80), (3) moderate risk (percentile 80 to percentile 90), (4) high risk (percentile 90 to percentile 95), and (5) very high risk (percentile >99).

Finally, in step 4, the correspondence between the MADS risk strata and health outcomes was analyzed through a cross-sectional analysis of use of health care resources, mortality, pharmacological burden, and health care expenditure and a longitudinal analysis of disease prevalence and incidence of new disease onsets. The results were validated through a multicentric replication of the findings in the 3 study cohorts including 1,041,014 individuals.

Step 1: Computing Age-Dependent PRs

BDMMs were used to assess direct and indirect associations between MDD and 86 potential comorbid conditions. The set of 86 disease conditions considered in the study had a prevalence of >1% in all the study cohorts. The list of diseases and their associated ICD-10-CM [ 29 ] codes are shown in Multimedia Appendix 1 .

This step considered information on disease diagnosis (disease conditions were cataloged using the first 3 characters of the ICD-10-CM codes), age at disease onset (the age at disease onset corresponds to the first diagnosis in a lifetime for each ICD-10-CM code), sex, and socioeconomic status (annual average total household income [before tax with copayment exemption] as a categorical variable with 3 categories: <€18,000 [US $19,565.30], €18,000-100,000 [US $19,565.30-108,696], and >€100,000 [US $108,696]).

BDMM analysis resulted in an inhomogeneous dynamic Bayesian network, which was used to compute temporal PR, ranging from 0 (no association) to 1 (strong association), for MDD in conjunction with sex, socioeconomic status, and the set of 86 predetermined diseases [ 30 ]. To construct the trajectories, the PR was calculated in 4 different age ranges: 0 to 20 years, 0 to 40 years, 0 to 60 years, and 0 to 70 years. The PRs calculated and used for MADS computation are reported in Multimedia Appendix 1 . Further details regarding the core analysis conducted in TRAJECTOME can be found in the study by Juhasz et al [ 30 ].

Step 2: Extracting and Aggregating Disease DWs

The MADS was developed by weighting the DWs of single diseases according to their estimated PRs against MDD. DWs indicate the degree of health loss based on several health outcomes and are used to indicate the total disability caused by a certain health condition or disease. Often, the DWs present specific disability scores tailored to the severity of the disease. The disease categories, severity distribution, and associated DWs used in this study were extracted from the GBD 2019 study and are reported in Multimedia Appendix 1 .

DWs were extracted and aggregated as follows. First, we considered only the DWs of MDD and the set of 86 disease codes. Second, we considered the DWs of all the chronic conditions diagnosed in patients’ lifetime, whereas as the disability caused by acute illnesses is transitory, the DWs for the acute diseases diagnosed >12 months before the MADS assessment were arbitrarily set to 0 (no disability). Third, due to the unavailability of information on the severity of diagnoses, we determined the DWs of each disease condition by calculating the weighted mean of the DWs associated with the disease severity categories and their prevalence. In instances in which the severity distribution was not available, we computed the arithmetic mean of the DWs of each severity category. Fourth, we weighted the DWs according to the PR of each disease condition with respect to MDD. The PRs were adjusted according to the age of disease onset, discretized in the following intervals: 0 to 20 years, 20 to 40 years, 40 to 60 years, and >60 years.

As the DWs do not account for multimorbidity in their estimates, the use of DW independently can cause inaccuracies in the burden of disease estimations, particularly in aging populations that include large proportions of persons with ≥2 disabling disease conditions [ 39 ]. Consequently, we combined the DW and the PR for all the disease conditions present in 1 individual following a multiplicative approach (equation 1) [ 40 ], aggregating several DWs in a single score that accounts for the overall disability caused by numerous concurrent chronic conditions in which every comorbid disease increases the utility loss of a patient, although it is less than the sum of the utility loss of both diseases independently.

In equation 1, “DW” stands for disability weight, “PR” stands for probability of relevance, and “n” is the number of diseases present in 1 individual.

The MADS pseudocode is reported in Multimedia Appendix 1 .

Step 3: Constructing the MADS Risk Pyramid

Once calculated, the MADS was used to stratify patients in different levels of risk according to the percentiles of its distribution in the source population, producing the following risk pyramid: (1) very low risk (percentile ≤50), (2) low risk (percentile 50-percentile 80), (3) moderate risk (percentile 80-percentile 90), (4) high risk (percentile 90-percentile 95), and (5) very high risk (percentile >99).

Step 4: Evaluating MADS Risk Strata

The clinical relevance of the risk strata was assessed through two interconnected analyses: (1) a cross-sectional analysis of health outcomes and (2) a longitudinal analysis of disease prevalence and incidence of new onsets.

Cross-Sectional Analysis of Health Outcomes and Use of Health Care Resources

To validate the results of the MADS, we conducted a cross-sectional analysis of clinical outcomes within the 12 months following the MADS assessment. The burden of MDD and its comorbidities on patients and health care providers, corresponding to each risk group of the MADS risk pyramid, was assessed using the following features (the parameters evaluated in each cohort may vary depending on the availability of the requested information in the source databases):

Prescriptions for psycholeptic and psychoanaleptic drugs (information available in all the databases)—the prescribed medication was cataloged using the first 4 characters from Anatomical Therapeutic Chemical classification [ 41 ] codes, resulting in the following categories: antipsychotics (N05A), anxiolytics (N05B), hypnotics and sedatives (N05C), and antidepressants (N06A)
Cost of the pharmacological prescriptions in euros (information available only in the CHSS and THL)
Mortality rates (information available only in the CHSS and THL)
Contacts and encounters with health care professionals (information available only in the CHSS), encompassing (1) primary care visits, (2) specialized care outpatient visits, (3) ambulatory visits in mental health centers, (4) emergency room visits, (5) planned and unplanned hospital admissions, and (6) admissions in mental health centers
Total health care expenditure (information available only in the CHSS), including (1) direct health care delivery costs; (2) pharmacological costs; and (3) other billable health care costs such as nonurgent medical transportation, ambulatory rehabilitation, domiciliary oxygen therapy, and dialysis

We assessed the effect of sex and age, replicating the analyses disaggregated by sex and age. The age ranges were discretized into the following categories: 0 to 20 years, 20 to 40 years, 40 to 60 years, and >60 years.

Longitudinal Analysis of Disease Prevalence and Incidence of New Onsets

To address the age dependency of disease onsets, we performed a longitudinal analysis of the prevalence of a target disease and the incidence of new diagnostics within the 5 years following the MADS assessment.

We iteratively computed the MADS in 5-year intervals throughout the patients’ lives. Within each interval, the population was stratified based on the MADS distribution. Subsequently, within each risk tier, the prevalence of the target disease and the incidence of new disease onset over the subsequent 5 years were calculated. Only individuals with complete information for the next interval at each time point of the analysis were included.

In the analysis, we considered only the chronic disease conditions with a PR against MDD of ≥0.80 in at least 1 of the 4 age intervals assessed, namely, 0 to 20 years, 0 to 40 years, 0 to 60 years, and 0 to 70 years. It resulted in the following set of mental diseases— MDD (F32-F33) , schizophrenia (F20) , bipolar disorder (F31) , anxiety-related disorders (F40-F41) , stress-related disorders (F43) , and mental disorders related to alcohol abuse (F10) —and the following somatic diseases: irritable bowel syndrome (K58) , overweight and obesity (E66) , and gastroesophageal reflux (K21) .

Data Sources

The study was conducted using data from 3 public health cohorts.

CHSS Cohort

The main cohort used in MADS development was extracted from the CHSS. Operated by a public single-payer system (CatSalut) [ 42 ] since 2011, the CHSS gathers information across health care tiers on the use of public health care resources, pharmacological prescriptions, and patients’ basic demographic data, including registries of 7.5 million citizens from the entire region of Catalonia (Spain). Nevertheless, for MADS development purposes, we considered only registry data from citizens residing in the entire Integrated Health District of Barcelona-Esquerra between January 1, 2011, and December 31, 2019 (n=654,913). To validate the results of the MADS, we retrieved additional information from the CHSS corresponding to the 12 months after the MADS assessment, from January 1, 2020, to December 31, 2020. It should be noted that all the deceased patients, in addition to those who moved their residence outside of the Integrated Health District of Barcelona-Esquerra between 2011 and 2019, were discarded from the MADS assessment analysis; the remaining subset of patients comprised 508,990 individuals.

The UKB data considered in this study contained medical and phenotypic data from participants aged between 37 and 93 years. Recruitment was based on National Health Service patient registers, and initial assessment visits were carried out between March 3, 2006, and October 1, 2010 (n=502,504). The analyzed data included disease diagnosis and onset time, medication prescriptions, and socioeconomic descriptors.

The THL cohort integrates information from the FINRISK [ 36 ] 1992, 1997, 2002, 2007, and 2012; FinHealth [ 37 ] 2017; and Health [ 38 ] 2000/2011 studies. For the consensual clustering, 41,092 participants were used from Finnish population surveys. After data cleaning, 30,961 participants remained. These participants, aged 20 to 100 years, were chosen at random from the Finnish population and represented different parts of Finland.

Demographic information on the study cohorts is shown in the Results section.

Ethical Considerations

As a multicentric study, TRAJECTOME accessed data from multiple cohorts, all subject to the legal regulations of their respective regions of origin, and obtained the necessary approvals from the corresponding ethics committees.

For the CHSS cohort, the Ethical Committee for Human Research at Hospital Clínic de Barcelona approved the core study of TRAJECTOME on March 24, 2021 (HCB/2020/1051), and subsequently approved the analysis for the generation and the assessment of the MADS on July 25, 2022 (HCB/2022/0720).

The UKB cohort received ethics approval from the National Research Ethics Service Committee North West–Haydock (reference 11/NW/0382).

The THL cohort integrates information from the FINRISK databases (1997 [ethical committee of the National Public Health Institute; statement 38/96; October 30, 1996], 2002 [Helsinki University Hospital, ethical committee of epidemiology and public health; statement 87/2001; reference 558/E3/2001; December 19, 2001], 2007 [Helsinki University Hospital, coordinating ethics committee; Dnro HUS 229/EO/2006; June 20, 2006], and 2012 [Helsinki University Hospital, coordinating ethics committee; Dnro HUS 162/13/03/11; December 1, 2011]), the FinHealth 2017 (Helsinki University Hospital, coordinating ethics committee; 37/13/03/00/2016; March 22, 2016), and the Health 2000 to 2011 databases (ethical committee of the National Public Health Institute, 8/99/12; Helsinki University Hospital, ethical committee of epidemiology and public health, 407/E3/2000; May 31, 2000, and June 17, 2011).

The ethics committees exempted the requirement to obtain informed consent for the analysis and publication of retrospectively acquired and fully anonymized data in the context of this noninterventional study.

All the data were handled in compliance with the General Data Protection Regulation 2016/679, which safeguards data protection and privacy for all individuals in the European Union (EU). The study was conducted in conformity with the Helsinki Declaration (Stronghold Version, Brazil, October 2013) and in accordance with the protocol and the relevant legal requirements (Law 14/2007 on Biomedical Research of July 3).

Statistical Analysis

The results of the cross-sectional analysis of health outcomes and use of health care resources were evaluated through various metrics. Mortality rates were summarized as cases per 1000 inhabitants. In contrast, numeric health outcome variables were described by the average number of cases per person, per 100 inhabitants, or per 1000 inhabitants according to their prevalence. Average health care expenditures were reported in euro per person. Kruskal-Wallis tests, supplemented with Bonferroni-adjusted post hoc right-tailed Dunn tests, and pairwise Fisher exact tests were used to evaluate changes in the target outcomes across the risk pyramid tiers. Statistical significance was determined by considering a P value of <.05 in all analyses.

The results of the longitudinal analysis on disease prevalence and on the incidence of new disease onsets of MDD and 9 mental and somatic MDD-related chronic conditions (PR>0.80) were expressed in percentages and in per thousand (‰), respectively.

All the data analyses were conducted using R (version 4.1.1; R Foundation for Statistical Computing) [ 43 ]. The MADS algorithm was fully developed and tested in the CHSS database and transferred to the other sites using an R programming executable script.

The study is reported according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) [ 23 ] guidelines for observational studies.

Sociodemographic Characteristics of the Study Cohorts

One of the first results was the characterization of the 3 study cohorts and comparison of the sociodemographic attributes of their MADS risk groups ( Table 1 ). All the individuals were classified into distinct risk strata based on quantiles of MADS distribution within the source population, resulting in the formation of the subsequent risk pyramid: very-low-risk tier (percentile ≤50), low-risk tier (percentile 50 to percentile 80), moderate-risk tier (percentile 80 to percentile 90), high-risk tier (percentile 90 to percentile 95), and very high–risk tier (percentile >99).

Risk pyramid tier and demographics				CHSS		THL		UKB

	Participants, N			507,549		30,961		502,504
	Age (y), mean (SD)			45.36 (23.07)		64.27 (14.28)		61.48 (9.31)

		Male	237,598 (46.8)		14,435 (46.61)		229,122 (45.6)
		Female	269,951 (53.2)		16,526 (53.39)		273,382 (54.4)

		Low (<€18,000 [US $19,565.30])	262,753 (51.76)		11,489 (37.1)		117,737 (23.42)
		Medium (€18,000-100,000 [US $19,565.30-$108,696])	223,369 (44)		10,025 (32.4)		358,492 (71.34)
		High (>€100,000 [US $108,696])	21,427 (4.24)		9447 (30.5)		26,275 (5.24)
	Major depressive disorder prevalence, n (%)			38,479 (7.58)		2287 (7.39)		53,466 (10.64)

	Participants, N			5651		310		5026
	Age (y), mean (SD)			55.74 (18.83)		68.83 (14.86)		61.7 (8.75)

		Male	2322 (41.09)		129 (41.61)		2207 (43.89)
		Female	3329 (58.91)		181 (58.39)		2819 (56.11)

		Low (<€18,000 [US $19,565.30])	4343 (76.86)		191 (61.61)		2285 (45.47)
		Medium (€18,000-100,000 [US $19,565.30-$108,696])	1251 (22.13)		77 (24.84)		2620 (52.13)
		High (>€100,000 [US $108,696])	57 (1.01)		42 (13.55)		121 (2.4)
	Major depressive disorder prevalence, n (%)			3870 (68.48)		186 (60)		4370 (86.94)

	Participants, N			22,894		1238		20,084
	Age (y), mean (SD)			60.08 (20.00)		65.12 (15.10)		63.2 (8.74)

		Male	7170 (31.32)		559 (45.23)		7545 (37.57)
		Female	15,724 (68.68)		679 (54.77)		12,539 (62.43)

		Low (<€18,000 [US $19,565.30])	14,568 (63.65)		690 (55.74)		7626 (37.97)
		Medium (€18,000-100,000 [US $19,565.30-$108,696])	7946 (34.7)		327 (26.41)		12,003 (59.76)
		High (>€100,000 [US $108,696])	380 (1.65)		221 (17.85)		455 (2.27)
	Major depressive disorder prevalence, n (%)			18,368 (80.27)		734 (59.29)		19,039 (94.78)

	Participants, N			84,371		4644		75,378
	Age (y), mean (SD)			54.56 (21.87)		68.86 (14.77)		63.6 (9.02)

		Male	34,462 (40.86)		2201 (47.4)		34,282 (45.48)
		Female	49,909 (59.14)		2441 (52.6)		41,096 (54.52)

		Low (<€18,000 [US $19,565.30])	49,818 (59.05)		2285 (49.24)		23,208 (30.77)
		Medium (€18,000-100,000 [US $19,565.30-$108,696])	32,822 (38.9)		1437 (30.93)		49,684 (65.93)
		High (>€100,000 [US $108,696])	1731 (2.05)		920 (19.83)		2486 (3.3)
	Major depressive disorder prevalence, n (%)			16,241 (19.25)		1367 (29.43)		25,776 (34.2)

	Participants, N			162,170		9,266		150,759
	Age (y), mean (SD)			47.66 (24.20)		66.16 (14.15)		62.2 (9.39)

		Male	77,082 (47.53)		4132 (44.58)		70,550 (46.72)
		Female	85,088 (52.47)		5137 (55.42)		80,209 (53.28)

		Low (<€18,000 [US $19,565.30])	85,936 (5300)		3623 (39.08)		36,773 (24.42)
		Medium (€18,000-100,000 [US $19,565.30-$108,696])	71,429 (44.06)		3081 (33.25)		106,441 (70.59)
		High (>€100,000 [US $108,696])	4805 (2.96)		2565 (27.67)		7545 (4.99)
	Major depressive disorder prevalence, n (%)			0 (0)		0 (0)		2002 (1.3)

	Participants, N			232,463		15,503		251,257
	Age (y), mean (SD)			38.72 (20.72)		61.62 (13.55)		60.3 (9.22)

		Male	116,562 (50.12)		7414 (47.83)		114,538 (45.59)
		Female	115,901 (49.88)		8088 (52.17)		136,719 (54.41)

		Low (<€18,000 [US $19,565.30])	108,088 (46.48)		4700 (30.32)		47,845 (19.04)
		Medium (€18,000-100,000 [US $19,565.30-$108,696])	109,921 (47.30)		5103 (32.92)		187,744 (74.72)
		High (>€100,000 [US $108,696])	14,454 (6.22)		5699 (36.76)		15,668 (6.24)
	Major depressive disorder prevalence, n (%)			0 (0)		0 (0)		2279 (0.9)

a The prevalence of depression was calculated considering both F32 and F33 International Classification of Diseases, 10th Revision, Clinical Modification diagnostic codes. Kruskal-Wallis tests were used to assess changes in the target outcomes according to the risk pyramid tiers (statistical significance: P <.05; H0=“all MADS risk groups have the same outcome distribution”; H1=“at least one MADS risk group has a different outcome distribution than the others”). P <.001 for age, sex, household income, and major depressive disorder prevalence for all cohorts.

It is imperative to underscore the fundamental distinctions in the cohorts under study to comprehend the inherent sociodemographic disparities across them. Specifically, the THL and UKB cohorts predominantly consist of data derived from biobanks, specifically focusing on the middle-aged and older adult population. In contrast, the CHSS cohort represents a population-based sample encompassing the entire population spectrum.

It is worth noting that a common pattern was observed among all the cohorts in the age distribution of the citizens at risk. Although the MADS is an additive morbidity grouper, it did not monotonically increase with age. Remarkably, a notable proportion of high-risk cases were observed within the age range of 40 to 60 years, when depression typically manifests for the first time on average.

A divergence in the sex distribution across the risk strata was observable and especially noticeable in the CHSS and UKB cohorts, where the morbidity burden associated with depression and its related diseases was amplified in women ( P <.001). Similarly, the disability caused by depression and its comorbidities was larger in families with fewer economic resources ( P <.001). Overall, the prevalence of MDD was greater in the UKB cohort than in the other cohorts. However, upon analyzing the allocation of the population with depression in the risk pyramid, a total of 57.79% (22,238/38,479) of individuals diagnosed with MDD were categorized in the “high”- and “very high” risk tiers in the CHSS cohort, whereas the proportion of individuals diagnosed with MDD who were allocated to the tip of the risk pyramid was 40.22% (920/2287) in the THL cohort and 43.78% (23,409/53,466) in the UKB cohort.

Assessment of the MADS Risk Groups

Assessment of the prs.

Analyzing the relationship between MDD and the morbidities assessed in the study is essential to interpreting the MADS risk strata. This analysis revealed various relevant connections between MDD and the diseases investigated, encompassing both acute and chronic conditions, with the latter being particularly noteworthy due to their nontransient nature. Notably, the cluster of mental and behavioral disorders showed the highest average PRs in depression. However, relevant associations also emerged among MDD and specific chronic somatic diseases affecting multiple organic systems ( Figure 2 ).

Use of Health Care Resources

The impact of MADS risk groups on health care systems was evaluated by investigating the correlation between the MADS risk categories and the use of health resources over the 12-month period following the MADS assessment within the CHSS cohort ( Table 2 ). The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including primary care visits ( P <.001), specialized outpatient visits ( P <.001), emergency room visits ( P <.001), hospital admissions ( P <.001), and ambulatory visits in mental health centers ( P <.001), as well as the pharmacological burden ( P <.001). Furthermore, the results of the pairwise comparisons between adjacent risk tiers illustrated a substantial and gradual pattern of increased health care use as individuals progress from lower MADS risk tiers to higher MADS risk tiers, reflecting an escalation in health care needs and requirements. Overall, patients with higher MADS scores exhibited a greater likelihood of experiencing morbidity-related adverse events, which subsequently leads to recurrent interactions with health care systems across multiple levels.

Risk pyramid tier	Primary care visits (visits per person)	Specialized outpatient visits (visits per person)	Emergency room visits (visits per 100 inhabitants)	Hospital admissions (admissions per 100 inhabitants)	Mental health visits (visits per 100 inhabitants)	Number of prescriptions (prescriptions per person)
Very high risk (percentile >99)	12.50	3.07	135.00	28.50	554.00	8.02
High risk (percentile 95 to percentile 99)	11.90	2.56	87.20	20.60	136.00	7.48
Moderate risk (percentile 80 to percentile 95)	9.03	1.82	61.90	14.50	44.20	5.11
Low risk (percentile 50 to tpercentile 80)	6.21	1.21	42.40	8.87	15.10	3.20
Very low risk (percentile ≤50)	2.96	0.50	23.40	3.25	5.96	1.07

a Kruskal-Wallis tests were used to assess changes in the target outcomes according to the risk pyramid tiers ( P value). Subsequent pairwise comparisons between each risk tier and the next level of less risk were conducted using right-tailed Dunn post hoc tests (statistical significance: P <.05).

b P <.001.

Mortality and Health Care Expenditure

We conducted a cross-sectional analysis investigating mortality rates and the health care expenditure within the 12 months following the MADS assessment, expressed as the average health care expenditure per capita and differentiating between pharmaceutical and nonpharmaceutical costs within the CHSS and THL cohorts ( Table 3 ). Significant variations in mortality rates were observed across the risk pyramid tiers ( P <.001), with rates in the high-risk strata being markedly elevated (ranging from 5 to 20 times depending on the cohort) compared to those for low-risk individuals. Furthermore, the distribution of average health care expenditures per person was significantly different among the risk tiers, with both pharmacological and nonpharmacological expenses demonstrating disparities ( P <.001). Pairwise comparisons further indicated that individuals at the highest-risk tier incurred substantially greater health care costs than those at the lowest tier, reflecting a gradient of financial impact correlated with increased risk levels.

Risk pyramid tier	Mortality (cases per 1000 inhabitants)			Pharmacological expenditure in euro per person, mean (SD)			Hospitalization expenditure in euro per person, mean (SD)			Total expenditure in euro per person—CHSS, mean (SD)
	CHSS	THL	CHSS		THL	CHSS		THL
Very high risk (percentile >99)	46.2	36.0	1214		966	539		270	12,517
High risk (percentile 95 to percentile 99)	41.5	33.7	772		1131	383		340	8404
Moderate risk (percentile 80 to percentile 95)	25.5	32.2	485		1077	270		254	5209
Low risk (percentile 50 to percentile 80)	11.5	14.8	292		810	165		185	3075
Very low risk (percentile ≤50)	2.57	7.3	99		363	60		123	1192

Pharmacological Burden

This study also examined the pharmacological burden on individuals after 12 months following the MADS assessment ( Table 4 ). The data analysis revealed distinct patterns of medication use across the risk tiers, with significant differences in the use of antidepressants, antipsychotics, anxiolytics, and sedatives ( P <.001 in all cases). This trend, consistently observed across the 3 cohorts, was further emphasized by pairwise comparisons between adjacent risk levels, which revealed a strong positive correlation between higher-risk strata and increased pharmaceutical consumption. This upward trend in medication use forms a clear gradient, demonstrating that individuals in progressively higher-risk tiers face substantially greater pharmaceutical needs.

Risk pyramid tier	Antipsychotics (N05A; prescriptions per person)				Anxiolytics (N05B; prescriptions per person)				Hypnotics and sedatives (N05C; prescriptions per person)				Antidepressants (N06A; prescriptions per person)
	CHSS	THL	UKB	CHSS		THL	UKB	CHSS		THL	UKB	CHSS		THL	UKB
Very high risk (percentile >99)	0.75	0.60	0.33	0.47		0.21	0.27	0.15		0.14	0.24	0.79		0.43	0.80
High risk (percentile 95 to percentile 99)	0.20	0.27	0.18	0.46		0.19	0.20	0.10		0.12	0.19	0.66		0.41	0.71
Moderate risk (percentile 80 to percentile 95)	0.07	0.08	0.15	0.28		0.08	0.16	0.05		0.10	0.18	0.27		0.27	0.54
Low risk (percentile 50 to percentile 80)	0.03	0.03	0.13	0.14		0.04	0.12	0.02		0.07	0.13	0.08		0.11	0.36
Very low risk (percentile ≤50)	0.01	0.01	0.11	0.04		0.02	0.09	0.01		0.04	0.10	0.02		0.06	0.26

a For recurrently dispensed medication, only the first prescription was considered in the analysis. Kruskal-Wallis tests were used to assess changes in the target outcomes according to the risk pyramid tiers ( P value). Subsequent pairwise comparisons between each risk tier and the next level of less risk were conducted using right-tailed Dunn post hoc tests. Statistical significance: P <.05.

To evaluate the influence of age and sex on the outcomes examined in this section, we replicated all the previously presented results categorizing the outcomes by sex and age and reported them in Multimedia Appendix 1 . The results suggest that the morbidity burden in individuals might be a primary driver influencing the occurrence of adverse health events and the heightened use of health care resources.

Multimorbidity Progression

We analyzed the prevalence and incidence of new MDD-associated diagnoses and the relevant comorbid conditions in 5-year intervals after the MADS assessment for depression throughout the patients’ life span ( Multimedia Appendix 2 ), allowing for a comprehensive examination of multimorbidity progression over time.

Multimedia Appendix 2 shows the current disease prevalences expressed in percentages and the incidence of new disease onsets across an interval of 5 years after the MADS assessment expressed in per thousand. Multimedia Appendix 2 also showcases the results for MDD and 9 mental and somatic MDD-related (PR>0.80) chronic conditions assessed independently in the 3 study cohorts, namely, CHSS, THL, and UKB, and in 4 time points, that is, ages of 20 years, 40 years, 60 years, and 70 years, corresponding to the intervals in which the PRs were recalculated. A continuous assessment of these outcomes is reported in Multimedia Appendix 1 .

In general, both MDD and the comorbid conditions investigated in this study exhibited a positive correlation between the MADS risk tiers and the current prevalence and incidence of new disease onsets within a subsequent 5-year interval. This is evident from the table. Notably, the highest disease prevalence and incidence values consistently appeared in the high- and very-high-risk tiers. In addition, there was a discernible pattern of well-stratified values across these risk tiers within the same age ranges, underlining significantly elevated prevalence rates of the studied diseases compared to the population average within the high-risk groups. Age also emerged as a pivotal determinant influencing disease onset, delineating unique patterns across various disorders. Notably, conditions such as gastroesophageal reflux and overweight consistently exhibited ascending trends in both incidence and prevalence throughout individuals’ life spans. Conversely, severe afflictions such as schizophrenia, bipolar disorder, and alcohol abuse reached their zenith in prevalence and incidence during middle-aged adulthood followed by a decline, possibly indicating an association with premature mortality. Moreover, anxiety- and stress-related disorders showed their highest incidence rates during youth and early adulthood.

The consistency of the findings illustrated in Multimedia Appendix 2 remained robust across all 3 study cohorts despite their significant demographic differences, described in Table 1 . These heterogeneities resulted in disease prevalence discrepancies among cohorts, as vividly portrayed in Multimedia Appendix 2 . Among the most relevant cases, there was an elevated prevalence of schizophrenia in the THL cohort in comparison with the CHSS and UKB cohorts. In this particular case, patients with schizophrenia constituted 100% of the very high–risk group in adulthood. Such differences in disease prevalence among cohorts may influence distinct health outcomes, particularly for the citizens allocated to the apex of the Finnish risk pyramid, as observed in the pharmacological and hospitalization expenditure outcomes reported in Table 3 .

Principal Findings

The MADS seems to provide a novel and more comprehensive understanding of the complex nature of depression-related multimorbidity. This approach recognizes that individuals with depression often experience a range of comorbid conditions that may manifest and evolve differently over time. By capturing this dynamic aspect, the MADS offers a nuanced assessment beyond a mere checklist of discrete disorders. The novelty of the MADS approach lies in its capability to serve as the first morbidity grouper that incorporates information on disease trajectories while improving the filtering of indirect disease associations using BDMMs.

In addition to capturing disease-disease associations, the MADS endeavors to gauge their impact within the system by leveraging well-established DWs. However, despite achieving success in fulfilling the study’s objectives, it is crucial to acknowledge that this approach carries inherent limitations, as will be elaborated on in the subsequent sections of this discussion.

In this investigation, we unearthed robust correlations between the MADS risk strata and the extent of deleterious impact caused by MDD and its comorbid conditions. Such associations indicate the presence of specific health risks and an escalated use of health care resources. Furthermore, a positive association emerged between the levels of pharmacological and nonpharmacological health care expenditures and the different tiers of MADS risk. In addition, the analysis revealed an augmented risk of disease progression within the high-risk groups (high and very high risk), as indicated by a heightened incidence of new-onset depression-related illnesses within a 12-month period after the MADS assessment. Similarly, mortality rates exhibited elevated values in these high-risk groups.

The findings presented in this study are underpinned by the complementary studies conducted within the TRAJECTOME project [ 30 ] that have established a better understanding of the complex multimorbidity landscape associated with MDD across an individual’s life span, encompassing modifiable and genetic risk factors.

Limitations of This Approach

Despite meeting expectations and validating the hypothesis through which the study was conceived, the authors acknowledge a series of limitations leading to suboptimal results and limited potential for adaptation and generalization that should be undertaken to bring the MADS, or an indicator derived from it, to short-term real-world implementation.

In this research, the use of estimations of mean DW [ 44 ] to assess the burden of disease conditions achieved desirable results and was conceptually justified, but it undoubtedly exhibited significant limitations. In an ideal clinical scenario, each disease diagnosis indicated in the patient’s electronic medical record should be characterized by three key dimensions: (1) severity of the diagnosis, (2) rate of disease progression, and (3) impact on disability. However, the degree of maturity for characterizing the last 2 dimensions—disease progression and disability—is rather poor because of the complexities involved in their assessment. In other words, the authors acknowledge the weakness associated with the current use of DW. However, they stress the importance of incorporating such dimensions in future evolutions of the MADS.

A noteworthy aspect that should be acknowledged is that factors such as the advancements in diagnostic techniques, the digitization of medical records, and the modifications in disease taxonomy and classification over time have contributed to a more exhaustive documentation of the disease states in the most recent health records. Consequently, this fact could lead to imprecisions in estimating the disease onset ages in older individuals.

Insights and Potential Impact of the MADS in Multimorbidity Management

The results reported in this study not only reaffirm the well-established link between multimorbidity and adverse outcomes, such as a decline in functional status, compromised quality of life, and increased mortality rates [ 45 ], but also shed light on the significant burden imposed on individuals and health care systems. From the population-based HRA perspective, the strain on resource allocation and overall health care spending is a pressing concern that necessitates effective strategies for addressing and managing multimorbidity [ 46 ]. In this context, assessing individual health risks and patient stratification emerge as crucial approaches that enable the implementation of predictive and preventive measures in health care.

While population-based HRA tools such as Adjusted Clinical Groups, Clinical Risk Groups, or AMG have traditionally addressed this aspect, the MADS is designed to complement rather than replace those tools. This study aimed to test a method to refine existing HRA tools by aligning them with the principles of network medicine, thereby merging traditional HRA with the practical application of network medicine insights. This innovative approach holds the promise of unlocking new potential advantages and capabilities.

The strength of the MADS approach lies in using disease-disease associations drawn from the analysis of temporal occurrence patterns among concurrent diseases. This virtue allows the MADS to refine the analysis of the morbidity burden by focusing on clusters of correlated diseases, which in turn can aid in developing more tailored epidemiological risk-related studies. This refined analysis might also assist in resource allocation and inform health care policies for targeted patient groups with specific needs. Moreover, this approach holds promise for potential extrapolation to other noncommunicable disease clusters such as diabetes, cardiovascular ailments, respiratory diseases, or cancer. By leveraging this targeted approach, the MADS can be adapted to other disease clusters with shared characteristics, enabling a more precise assessment of disease burden and comorbidity patterns and thereby generating multiple disease-specific indexes.

Notably, when considering information derived from disease co-occurrence patterns, the presence or absence of certain diseases seems to correlate with the risk of developing related comorbid conditions, as elucidated in Multimedia Appendix 2 . This highlights the potential for a nuanced understanding of disease relationships and their impacts on health outcomes and to implement preventive interventions to mitigate their effect. Moreover, the findings of this study highlight the potential of preventive strategies targeted at mental disorders, including substance abuse disorders, depressive disorders, and schizophrenia, to reduce the incidence of negative clinical outcomes in somatic health conditions. These important implications for clinical practice call for a comprehensive and interdisciplinary approach that bridges the gap between psychiatric and somatic medicine. By developing cross-specialty preventive strategies, health care professionals can provide more holistic and effective care for individuals with complex health needs, ensuring that their mental and physical health are adequately addressed [ 47 ].

This study provided good prospects of using disease trajectories to enhance the performance of existing state-of-the-art morbidity groupers such as AMG. Recognized for its transferability across EU regions by the EU Joint Action on implementation of digitally enabled integrated person-centered care [ 48 ], AMG stands out due to its stratification capabilities, adaptability, and distribution as open-source software, providing several advantages over its commercial counterparts. The AMG system uses disease-specific weighting derived from statistical analysis incorporating mortality and health care service use data. This method addresses the primary drawback identified in the MADS approach inherent to the use of DW while enabling the development of adaptable tools that align with the unique characteristics of each health care system. Consequently, it allows for the adjustment to the impact of specific disease conditions within distinct regions and enhances the overall applicability and adaptability of the tool. In this regard, this study offered promising insights aligned with the developers’ envisioned future features for integration into the AMG system. Serving as a proof of concept, it highlighted the potential improvements achievable within AMG by leveraging disease-disease associations, thereby shaping the road map for further AMG development.

MADS Integration in Precision Medicine: Advancing Toward Patient-Centric Strategies

By assessing whether the MADS is appropriate for the stratification of depression-related multimorbidity, we attempted to confirm its potential for contributing to precision medicine [ 49 ]. In the clinical arena, identifying individuals at elevated risk and customizing interventions enable health care providers to intervene proactively, potentially preventing or lessening disease progression and enhancing patient outcomes. These strategies not only yield immediate value in terms of improved patient care but also lay the foundation for the broader adoption of integrated care and precision medicine, particularly in the management of chronic conditions [ 50 ].

Incorporating systems medicine [ 51 ] methodologies and ITs has prompted significant shifts in clinical research and practice, paving the way for holistic approaches, computational modeling, and predictive tools in clinical medicine. These advancements are driving the adoption of clinical decision support systems, which use patient-specific data to generate assessments or recommendations, aiding clinicians in making informed decisions. It is well established that, to improve predictive precision and aid clinical decision-making, implementing comprehensive methodologies that consider various influencing factors from multiple sources in patient health could enhance individual prognosis estimations [ 52 ].

This integration might facilitate predictive modeling methodologies for personalized risk prediction and intervention planning. This approach, known as multisource clinical predictive modeling [ 53 , 54 ], enables the integration of (1) health care data and health determinants from other domains, including (2) population health registry data; (3) informal care data (including patients’ self-tracking data, lifestyles, environmental and behavioral aspects, and sensors); and, ideally, (4) biomedical research omics data. In this paradigm, it is crucial to acknowledge the pivotal role that multimorbidity groupers play in capturing the clinical complexity of individuals. Previous research [ 53 , 54 ] has highlighted the synergy between patient clinical complexity (eg, AMG) and acute episode severity, correlating with higher risks of adverse health events. This opens avenues for further research, exploring how adjusted morbidity indicators such as the MADS can significantly contribute to predictive modeling, aiming at supporting the implementation of cost-effective, patient-centered preventive measures to manage patients with chronic diseases and potentially delay or prevent their progression to the highest-risk levels in the stratification pyramid [ 55 ].

Conclusions

The MADS showed to be a promising approach to estimate multimorbidity-adjusted risk of disease progression and measure MDD’s impact on individuals and health care systems, which could be tested in other diseases. The novelty of the MADS approach lies in its unique capability to incorporate disease trajectories, providing a comprehensive understanding of depression-related morbidity burden. In this regard, the BDMM method played a crucial role in isolating and identifying true direct disease associations. The results of this study pave the way for the development of innovative digital tools to support advanced HRA strategies. Nevertheless, clinical validation is imperative before considering the widespread adoption of the MADS.

Acknowledgments

This initiative was supported by European Research Area on Personalized Medicine (ERA PerMed) program (“Temporal disease map based stratification of depression-related multimorbidities: towards quantitative investigations of patient trajectories and predictions of multi-target drug candidates” [TRAJECTOME] project; ERAPERMED2019-108). Locally, this study was supported by the Academy of Finland under the frame of the ERA PerMed program and the Hungarian National Research, Development, and Innovation Office (2019-2.1.7-ERA-NET-2020-00005K143391, K139330 and PD 134449 grants); the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and the Ministry for Innovation and Technology of Hungary from the National Research, Development, and Innovation Fund under the TKP2021-EGA funding scheme (TKP2021-EGA-25 and TKP2021-EGA-02). This study was supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. The authors want to acknowledge the earnest collaboration of the Digitalization for the Sustainability of the Healthcare System research group at Institut d'Investigació Biomèdica de Bellvitge (IDIBELL) for their support in the preparation of the Catalan cohort, which was extracted from the Catalan Health Surveillance System database, owned and managed by the Catalan Health Service. In addition, the authors want to acknowledge the participants and investigators of the FinnGen study and CSC–IT Center for Science, Finland, for computational resources. This research was conducted using the UK Biobank resource under application 1602. Linked health data Copyright 2019, NHS England. Reused with the permission of the UK Biobank. All rights reserved.

Data Availability

The data sets generated during and analyzed during this study are not publicly available due to patient privacy concerns. The scripts used to compute the Multimorbidity-Adjusted Disability Score are available from the corresponding author upon reasonable request.

Authors' Contributions

PA, GJ, and IC designed the study and directed the project. RG-C, KM, and IC led the design of the Multimorbidity-Adjusted Disability Score. RG-C, KM, AG, and TP executed the quantitative analysis, processed the experimental data, performed the statistical analysis, and created the figures. EV generated the Catalan Health Surveillance System database and provided statistical support. ZG, GH, HM, TN, MK, JPJ, and JR provided insightful information to the study. The manuscript was first drafted by RGC, IC, and JR and thoroughly revised by KM, EV, AG, TP, ZG, GH, HM, TN, MK, JP-J, PA, and GJ. All authors approved the final version of the manuscript and are accountable for all aspects of the work in ensuring its accuracy and integrity.

Conflicts of Interest

None declared.

Supplementary material encompassing the tables and figures from the cross-sectional and longitudinal analyses of outcomes, along with the disability weights and probabilities of relevance, as well as the Multimorbidity-Adjusted Disability Score (MADS) pseudocodes.

Longitudinal analysis of disease prevalence and incidence of new disease onsets in the Catalan Health Surveillance System, UK Biobank, and Finnish Institute for Health and Welfare.

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Abbreviations

Adjusted Morbidity Groups

Bayesian direct multimorbidity map

Catalan Health Surveillance System

disability weight

European Union

Global Burden of Disease

International Classification of Diseases, 10th Revision, Clinical Modification

Multimorbidity-Adjusted Disability Score

major depressive disorder

probability of relevance

Finnish Institute for Health and Welfare

UK Biobank

Edited by A Mavragani; submitted 27.09.23; peer-reviewed by R Meng, C Doucet; comments to author 02.11.23; revised version received 23.11.23; accepted 23.05.24; published 24.06.24.

©Rubèn González-Colom, Kangkana Mitra, Emili Vela, Andras Gezsi, Teemu Paajanen, Zsófia Gál, Gabor Hullam, Hannu Mäkinen, Tamas Nagy, Mikko Kuokkanen, Jordi Piera-Jiménez, Josep Roca, Peter Antal, Gabriella Juhasz, Isaac Cano. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Depression in Pakistan: An epidemiolgical critique

The epidemiological studies from Pakistan have given rise conflicting findings. Besides very high prevalence in different studies, rates from Northern Pakistan are much different from big urban centre such as Karachi. If the findings of these studies are to be taken at face value than every third Pakistani is expected to be suffering from depression and Anxiety. Obviously this has serious implications for the country’s mental health care scenario. There are design, sampling and methodological issues which needs to be revisited. This review aims to This review presents a critique, from an epidemiological perspective, on studies carried out in Pakistan on estimating rates and risk factors of depression. It is expected that this critique will serve to enhance awareness on research methods in psychiatry and suggest future directions for research in this important area.

Key words: Depression, Psychiatric Epidemiology, Pakistan.

INTRODUCTION

Psychiatric Epidemiology is a discipline that deals with methodological issues of measurement, i.e. case definition and case identification, psychometric properties, study design and samples, and theoretical models of environment and genetic origins of psychopathology. This review presents a critique, from an epidemiological perspective, on studies carried out in Pakistan on estimating rates and risk factors of depression. It is expected that this critique will serve to enhance awareness on research methods in psychiatry. A general discussion on various measures of disease morbidity is firstly presented followed by specific critique on epidemiological evidence on depression in Pakistan. This may appear too basic but will help to put the findings in context.

Measures of Disease Morbidity

In epidemiology, the most important tool for measuring disease is the rate, but ratio and proportions are also used. A ratio expresses the relationship between two numbers in the form x:y or x/y X k. A proportion is a specific type of ratio in which the numerator is included in the denominator, and the resultant value is expressed as a percentage. A rate is a special form of proportion that includes specification of time. The rate is the basic measure of disease occurrence because it is the measure that most clearly expresses probability or risk of disease in a defined population over a specified period of time 1 .

In order to calculate rate, we must be able to count accurately all events of interest that occur in a defined population during a specific period of time. A number of different rates of morbidity, or illness, are used in public health and epidemiology. All fall into two basic types, rates of incidence and rates of prevalence.

Incidence rates measure the probability that healthy people will develop a disease during a specific period of time; hence, it is the number of new cases of a disease in a population over a period of time. Incidence rates are a measure of probability or risk of disease (conditional on the individual’s not dying from any cause). Risk can vary from zero to one, is dimensionless, and requires a specific period referent. The most common way to estimate risk is to divide the number of newly detected cases that developed during follow-up by the number of disease-free subjects at the start of follow-up; a proportion called as cumulative incidence (CI) 1 . Importantly, determination of date of onset is necessary for studies of incidence. For some events, this determination is relatively simple. The onset of influenza, acute myocardial infarction, gastroenteritis can often be pinpointed to specific hour. However, this is not true of certain psychiatric conditions, whose onset may be insiduous and difficult to define..

The Prevalence rate measures the number of people in a population who have the disease at a given time. Prevalence measures the probability of people having a disease at a given point in time (more specifically termed as point prevalence). Prevalence depends on two factors: the number of people who have been ill in the past (i.e. previous incidence) and the duration of the illness. The relation of prevalence (P) to both incidence (I) and duration (d) of disease is expressed in the formula P ~ I x d, which states that prevalence varies directly with both incidence and duration 1 .

In contrast to incidence, high prevalence does not necessarily signify high risk; it may merely reflect an increase in survival, thus giving a biased picture. This difference is crucial to an understanding of screening programs. The first screening of a population picks up prevalent as well as incident cases of disease. Rescreening detects only incident cases (i.e. those that developed between the first and subsequent screens). It is important to remember that cross-sectional surveys (even if repeated over some time) do not constitute a longitudinal study and, therefore, do not permit etiological inference or estimates of changes in risk of disease over time.

Reliability: Kappa Statistics

Kappa quantifies the extent to which the observed agreement that the observers achieved exceeds that which would be expected by the chance alone, and expresses it as the proportion of the maximum improvement that could occur beyond the agreement expected by chance alone. It can be defined in an equation:

Kappa = (Percent agreement observed) – (Percent agreement expected by chance alone)

100% - (percent agreement expected by chance alone)

For example, if two psychiatrist are asked to assess 50 patients for probable depressive disorder. Each psychiatrist will classify individual case as depressed or not-depressed or Normal (For the sake of clarity we will assume that individual patient has no other comorbidity).

Following 2x2 can be constructed:

Agreement between two psychiatrists: 16+ 28/50 = 88%

For A Depressed = 18/50 = 0.36,

For B Depressed = 20/50 = 0.40

Agreement expected by chance for depression = 0.36x 0.40= 0.144

For A Normal 32/50= 0.64, For B Normal 30/50 = 0.6

Agreement expected by chance for normal = 0.64x0.6 = 0.384

Total chance agreement = 0.144+0.384= 0.528

Kappa=P0_Pc/1-Pc K=0.88–0.528/1–0.528=0.75.

P0= Observed agreement K < 0.4, Poor agreement

Pc= Agreement expected by Chance 0.4 – 0.75, Moderate agreement; 0.75 or > very good to excellent.

Validity Issues: Sensitivity and specificity

Strength of studies carried out by Mumford et al is two stage screening method. In their initial assessment they have screened the population using screening instrument (Bradford Somatic inventory - BSI) for depression followed by a structured psychiatric interview (Present State Examination- PSE-9). There are some issues (strengths and weaknesses) in choice of sequential use of two screening instruments. In sequential screening only those patients, who score positive on the first stage, are enrolled for further tests. Those who score negative on the test might have a disease and are liable to be misclassified, given the specific validity of the screening instrument. An evaluation of those who score negatively on the test, although some what expensive, is likely to give an indication of this misclassification.

Sequential use of two instruments, however, increases the net specificity. In sequential or two-stage screening, a less expensive, less invasive or less uncomfortable test is generally performed first, and those who screen positive are recalled for further testing with a more expensive, more invasive, or more uncomfortable test, which may have greater sensitivity and specificity. It is expected that bringing back those who test positive, for further testing, will reduce the problem of false positive. However this will result in loss of sensitivity at the cost of increase in specificity.

In order to understand it further, considers a hypothetical example; if disease prevalence in a study is given as 30%, so that in the population of 10,000, 3000 persons have the disease. With a sensitivity of 70%, the test will correctly identify 2100 of the 3000 people who have the disease. With a specificity of 80%, the test will correctly identify as non-depressed (Normal) 5600 of the 7000 people who are free of depressive disorders; however 1400 of these 7000 will have positive results. Thus a total of 3500 people will test positive and will be brought back for a second test.

Prevalence of depression from community studies in Pakistan

Now those 3500 people are brought back and screened using a second test (such as PSE), which for purpose of this example is assumed to have a sensitivity of 90% and a specificity of 90%. 2x2 table shows that test 1 together with test 2, which deal only with 3500 people who tested positively in the first screening test and have been brought back for second-stage screening. Since 2100 people (of the 3500) have the disease and test has a sensitivity of 90%, 1890 of those 2100 will be correctly identified as positives. Because 1400 (of the 3500) do not have depression and the test specificity is 90%, 1260 of the 1400 will be correctly identified as negative and 140 will be false positives. We can now calculate the net sensitivity and the net specificity of using both tests in sequence.

Example: Assume; Disease Prevalence = 30%, Population = 10,000

Test 1: (screening questionnaire for Depression) Sensitivity; 70%, Specificity; 80%

Test 2 (structured interview) Sensitivity; 90%, Specificity 90% DEPRESSION

Net sensitivity = 1890/3000 = 63%

Net specificity = 5600 + 1260/ 7000 = 98%

Prevalence Estimates of Depression and Anxiety from Pakistan

There are five community based studies reporting prevalence estimates for Depression and Anxiety from various regions of Pakistan (See table 1). These studies give variable prevalence estimates of Depression; from as high as 66% in women from rural areas to 10% in men from urban areas. The mean overall point prevalence is 33.62% (n=2658) 2 . These hand full of studies, along with few other center based studies, comprises the epidemiological evidence for Common mental disorders from Pakistan.

A critical re-evaluation of these studies is required given the variability of findings. There are design, sampling and methodological issues which needs to be revisited. If the findings of these studies are to be taken at face value than every third Pakistani is expected to be suffering from depression and Anxiety. Obviously this has serious implications for the country’s mental health care scenario. Rates from Northern Pakistan are much different from Karachi. Is this an artifact or the low rates of depression and anxiety from Karachi could be ascribed to the systematic error of center based sampling methodology?

Prevalence Rates from Karachi:

In a study carried out in semi-urban squatter settlements of Azam Basti, Karachi, Ali (2000) reported an apparent prevalence (proportion) of 30% in study population. Crude estimates for males were 18.1% and for females 42.2% 3 . Absence of age-adjusted rates and lack of validated screening instruments raise certain methodological issues. The participants were interviewed by four Consultant Psychiatrists on weekends. Diagnosis was based on DSM-III R criteria. Authors made no mention of inter-rater reliability or the level of agreement among the group. Lack of either screening instrument or a structured interview brings the issue of case ascertainment in to question. The literature during 1980s and 90s is replete with references, to the lack of reliability in clinical judgment when using the categorical approach for case definition.

Other methodological limitation of this study is that only those individuals were included who could understand the National Language, Urdu. This could have a major selection bias as Azam Basti is an area which has large number of immigrants. These people are not expected to be fluent in Urdu. Azam Basti, like other field sites of Department of Community health Sciences, Aga Khan University Hospital is a semi-urban squatter settlement. Squatter settlements like Azam Basti, Hijrat Colony, and Bilal Colony cater to large influx of immigrant population from various parts of the country. In a recent random house hold survey of Bilal colony (n=425), 40% were identified to be Punjabi, 27% Pathans, 16% Sindhis and 9% Urdu speaking 4 . According the last census 22.1% of the Karachi city’s population are migrants from other places. Therefore study by Ali gives prevalence rates in a selected sub-group of Urdu speaking population residing in the semi-urban squatter settlement.

Another selection-bias was the recruitment of only those patients who could attend the primary health care center. Author describes this limitation as “a randomized house hold survey could not be conducted as most of the households had only one or two rooms and taking permission to enter the house holds and ensuring privacy could not be arranged” 3 . However, from an epidemiological point of view, the denominator of proportions and rates may not be population in the usual sense.

Prevalence rates from Northern Pakistan:

Another series of paper by Mumford et al describes the prevalence estimates for depression and anxiety from Northern Punjab. The apparent point prevalence of depression from Urban Rawalpindi was found to be 25% for women and 10% for men 5 . In another study, using a similar methodology, the prevalence estimate from rural community setting of Rawalpindi was 57.5% for women and 25.5% for men 6 . One of the limitations in the analysis phase of the study and subsequent presentation is absence of age cutoff for geriatric population. Authors reported the prevalence estimates on any subject older than eighteen 6 . It is well known that prevalence estimate for many illnesses increases with the age besides the presenting features of the illness. Similarly a much higher estimate was found by the same author from mountainous region of Chitral in Northern Pakistan. Depression was estimated to be 25% to 72% among women and 10% to 44% among men 7 .

The wide variation in estimates raises certain validity issues of screening tests. Validity of any test or screening instrument is defined as its ability to distinguish between who has a disease and who does not.

Validity is a component of sensitivity and specificity of a test. Next section addresses these issues from an epidemiological point of view.

Establishing causality: Risk factors for Depression

In order to determine antecedents of disease, it is necessary to establish a time sequence and show that presumed independent variable(s) antecede the dependent one. Such temporal relationship can not be established by cross sectional data 8 . It is important to keep this limitation in mind because it is tempting to use prevalence data for causal inference, since they are more readily obtained than incidence data. So how do we determine whether a certain disease is associated with a certain exposure? To determine whether such an association exists, we must determine, using data obtained in case-control and cohort studies, whether there is an excess risk of the disease in persons who have been exposed to certain agents as opposed to risk in unexposed population.

Review of studies carried out on depression in Pakistan shows that all are cross sectional studies in design. These studies report various risk factors for depression in studied population. Some are congruent to studies in the west while other gives contrary evidence. Rates for depressive disorder are reported to be higher in women than men. This is consistent with the estimates from western countries. However one disparity that is observed is significantly higher rates in married than single females. Literature, from western countries, considers marriage to be a protective factor. It can be hypothesized that there are socio-cultural stressors specific to Pakistani culture that renders married females vulnerable to depression. One can ask; is this factual or an artifact of measurement?

In a cross sectional study carried out by Ali and Naeem in urban middle class population of Karachi, looking specifically at the psychosocial risk factors, found extended family systems to be a particular risk factor 9 . However findings from Northern Punjab are contradictory 6 . It reports extended family networks as a protective factor for married females. Other reported risk factors for depression from Pakistan are low level of education, poverty and economic constraints.

Landmark study by social scientists from U.K (Brown and Harris, 1978) reports emotional burden of child-care, non confiding relation with husband and nonprofessional status (having no job outside the home) as vulnerability factor for depression among females 10 . In an earlier study (Naeem S) these risk factors were replicated besides the stressor of hostile in-laws 11 . Contrarily studies from Northern Punjab reports that risk factors identified by Brown and Harris in London do not seem to apply for women in Punjab.

Another identified risk factor for Depression is socio-economic status. It is a complex concept that has been borrowed by medical researchers, often without due regard to its sociological inheritance. In epidemiology the concept is assessed indirectly using a variety of different measures with different implications for social and economic policy. Income, material possessions (or standard of living), occupational status, and education are the domains most commonly studied 12 . Nevertheless, these measures are not equivalent and might have different meanings and represent different concepts of social position in different cultures. For instance, income changes throughout life while education remains comparatively “frozen” after early adulthood and educational attainments can have different meaning in different places. The association between relative or absolute income and health is among the most commonly reported in the scientific literature. However, recent studies from western countries, using robust deigns, have found that this association is weakened or disappear when controlling for other socioeconomic variables, especially education 13 .

In conclusion all of the studies carried out in Pakistan were cross sectional in design. Given the limitation of the study design, it remains unclear what exposure acts as a risk factor for depression. Univariate and covariate analysis of data can give putative risk factors which can be subsequently tested using multivariate regression analysis. However, one need to be careful, regression analysis has its limitation in handling complicated data. In order to build a model one needs adequate sample per variable (at least 10) besides the significance and sequence of individual variables, which have a bearing on the final model of regression analysis 14 . Longitudinal studies need to be carried out in order to establish robust evidence on incidence and riskfactors of depression in Pakistan.

REFERENCES 1. Gordis, L. Assessing the validity and reliability of diagnostic and screening tests, In: L. Gordis (ed.), Epidemiology, 3rd ed. Philadelphia, Elsevier Saunders; 2004; p. 71-940. 2. Mirza I, Jenkins R. Risk factors, prevalence, and treatment of anxiety and depressive disorders in Pakistan: systematic review. BMJ 2004; 328:794-7. 3. Ali BS, Amanullah S. Prevalence of Anxiety and Depression in an Urban Squatter settlement of Karachi. J Coll Physicians Sur Pak 2000; 10:4-6. 4. Khawaja MR, Mazahir S, Majeed A, Malik F, Merchant KA, Maqsood M, et al. Chewing of Betel, Areca and Tobacco: Perceptions and Knowledge Regarding their Role in Head and Neck Cancers in an Urban Squatter Settlement in Pakistan. Asian Pac J Cancer Prev 2006; 7: 95-100 5. Mumford DB, Minhas FA, Akhtar I, Akhtar S, Mubbashar MH. Stress and Psychiatric disorder in Urban Rawalpindi: a community survey. Br J Psychiatry 2000; 177:557-62. 6. Mumford DB, Saeed K, Ahmad I, Latif S, Mubbashar MH. Stress and psychiatric disorder in rural Punjab: a community survey. Br J Psychiatry 1997; 170:473-8. 7. Mumford DB, Nazir M, Jilani FU, Baig IY. Stress and psychiatric disorder in Hindukush: a community survey of mountain village. Br J Psychiatry 1996; 168:299-307. 8. Grimes DA, Schulz KF. Descriptive studies: what they can and cannot do. Lancet 2002; 359: 145-9. 9. Ali BS, Rahbar MH, Naeem S, Tareen AL, Gul A, Samad L. Prevalence of and factors associated with anxiety and depression among women in a lower middle class semi-urban community of Karachi, Pakistan. J Pak Med Assoc 2002; 52:513-7. 10. Brown GW, Harris TO: Social Origins of Depression: A Study of Psychiatric Disorder in Women. London, Tavistock, 1978. 11. Naeem S. Psychosocial risk factors for depression in Pakistani women. [Dissertation] Karachi: College of Physicians and Surgeons of Pakistan, 1990. 12. Ellison G. How robust is the association between income distribution and health? J Epidemiol Community Health 1998; 52:694. 13. Muller A. Education, income, and mortality: a multiple regression analysis. BMJ 2002; 324:1–4.

14. Katz MH. Multivariable analysis: A practical guide for clinicians. Cambridge: Cambridge University press; 1999.

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Depression in a Pakistani Woman

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Risk factors, prevalence, and treatment of anxiety and depressive disorders in Pakistan: systematic review

Introduction

Data sources

Study selection

Data extraction

Validity check

Study synthesis

Methods of included studies

Prevalence of anxiety and depressive disorders

Associated social, psychological, and biological factors

What is the evidence for effectiveness of treatment or prevention in this population?

Limitations of study

Comparison with other low income countries

Possible reasons for our findings

The need for stronger evidence and improved research capacity

What is already known on this subject

What this study adds

SYSTEMATIC REVIEW article

Introduction

Pakistan-a Context

Study Design

Participants, Intervention Comparators

Systematic Review Protocol

Literature Search Strategy and Data Sources

Studies Selection and Data Extraction

Risk of Bias

Data Analysis

Study Selection Process

Characteristics of Included Studies

Synthesized Findings

Subgroup Analyses

Meta Regression Analysis

Sensitivity Analysis

Assessments of Publication Bias

Summary of Main Findings

Strengths and Limitations

Data Availability Statement

Author Contributions

Conflict of Interest

Supplementary Material

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